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  • 1.
    Alneberg, Johannes
    et al.
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Bjarnason, Brynjar Smári
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    de Bruijn, Ino
    Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm, Sweden.
    Schirmer, Melanie
    School of Engineering, University of Glasgow, Glasgow, UK.
    Quick, Joshua
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK; National Institute for Health Research (NIHR), Surgical Reconstruction and Microbiology Research Centre, University of Birmingham, Birmingham, UK.
    Ijaz, Umer Z.
    School of Engineering, University of Glasgow, Glasgow, UK.
    Lahti, Leo
    Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland; Laboratory of Microbiology, Wageningen University, Wageningen, the Netherlands.
    Loman, Nicholas J
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
    Andersson, Anders F
    Science for Life Laboratory, School of Biotechnoloy, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Quince, Christopher
    School of Engineering, University of Glasgow, Glasgow, UK.
    Binning metagenomic contigs by coverage and composition2014In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 11, no 11, p. 1144-6Article in journal (Refereed)
    Abstract [en]

    Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

  • 2.
    Antila, Kari
    et al.
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Lötjönen, Jyrki
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Thurfjell, Lennart
    GE Healthcare, Stockholm, Sweden.
    Laine, Jarmo
    Nexstim Ltd, Helsinki, Finland.
    Massimini, Marcello
    University of Milan, Milan, Italy.
    Rueckert, Daniel
    Imperial College London, London, United Kingdom.
    Zubarev, Roman A.
    Karolinska Institutet, Stockholm, Sweden.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Tampere, Finland.
    van Gils, Mark
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Mattila, Jussi
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Hviid Simonsen, Anja
    Rigshospitalet, Copenhagen, Denmark.
    Waldemar, Gunhild
    Rigshospitalet, Copenhagen, Denmark.
    Soininen, Hilkka
    University of Eastern Finland, Kuopio, Finland.
    The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease2013In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 3, no 2, article id 20120072Article in journal (Refereed)
    Abstract [en]

    Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

  • 3.
    Cao, Yang
    et al.
    Örebro University, School of Medical Sciences. Örebro University Hospital. Clinical Epidemiology and Biostatistics.
    Moe, S. Jannicke
    Norwegian Institute for Water Research (NIVA), Oslo, Norway.
    De Bin, Riccardo
    Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
    Tollefsen, Knut Erik
    Norwegian Institute for Water Research (NIVA), Oslo, Norway; Centre for Environmental Radioactivity (CERAD), Norwegian University of Life Sciences (NMBU), Ås, Norway; Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Ås, Norway.
    Song, You
    Norwegian Institute for Water Research (NIVA), Oslo, Norway.
    Comparison of piecewise structural equation modeling and Bayesian network for de novo construction of a quantitative adverse outcome pathway network2023In: Altex, ISSN 1868-596X, E-ISSN 1868-8551, Vol. 40, no 2, p. 287-298Article in journal (Refereed)
    Abstract [en]

    Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to the predictive nature, alignment with quantitative risk assessment and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and Bayesian network were suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches were capable of identifying the most influencing linear AOP in a complex network and evaluating the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The PROs and CONs of the two approaches for qAOPN construction were discussed in detail and suggestions on optimal workflows of PSEM and BN were provided to guide future qAOPN development.

  • 4.
    De Maeyer, Dries
    et al.
    Center of Microbial and Plant Genetics, Leuven, Belgium .
    Renkens, Joris
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    Cloots, Lore
    Center of Microbial and Plant Genetics, Leuven, Belgium .
    De Raedt, Luc
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    Marchal, Kathleen
    Center of Microbial and Plant Genetics, Leuven, Belgium ; Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium .
    PheNetic: Network-based interpretation of unstructured gene lists in E. coli2013In: Molecular Biosystems, ISSN 1742-206X, E-ISSN 1742-2051, Vol. 9, no 7, p. 1594-1603Article in journal (Refereed)
    Abstract [en]

    At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network.

  • 5.
    de Mas, Igor Marin
    et al.
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain;; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Selivanov, Vitaly A.
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain; A.N.Belozersky Institute of Physico-Chemical Biology, Moscow, Russia.
    Marin, Silvia
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain;; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Roca, Josep
    Hospital Clínic, August Pi i Sunyer Biomedical Research Institute (IDIBAPS),Centro de Investigación Biomédica en Red de Enfermedade Respiratorias (CIBERES) Universitat de Barcelona, Barcelona, Spain.
    Oresic, Matej
    Technical Research Centre of Finland, Espoo, Finland; Institute for Molecular Medicine, Helsinki, Finland.
    Agius, Loranne
    Institute of Cellular Medicine, The Medical School, Newcastle University, Newcastle, UK.
    Cascante, Marta
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions2011In: BMC Systems Biology, E-ISSN 1752-0509, Vol. 5, article id 175Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution.

    RESULTS: The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells.

    CONCLUSIONS: The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

  • 6.
    Eklund, Martin
    et al.
    Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; AstraZeneca Research and Development, Mölndal, Sweden.
    Norinder, Ulf
    H Lundbeck & Co AS, Valby, Denmark.
    Boyer, Scott
    AstraZeneca Research and Development, Mölndal, Sweden.
    Carlsson, Lars
    AstraZeneca Research and Development, Mölndal, Sweden.
    Choosing Feature Selection and Learning Algorithms in QSAR2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, no 3, p. 837-843Article in journal (Refereed)
    Abstract [en]

    Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

  • 7.
    Forreryd, Andy
    et al.
    Department of Immunotechnology, Lund University, Lund, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institute, Unit of Toxicology Sciences, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Lindberg, Tim
    Department of Immunotechnology, Lund University, Lund, Sweden.
    Lindstedt, Malin
    Department of Immunotechnology, Lund University, Lund, Sweden.
    Predicting skin sensitizers with confidence: Using conformal prediction to determine applicability domain of GARD2018In: Toxicology in Vitro, ISSN 0887-2333, E-ISSN 1879-3177, Vol. 48, p. 179-187Article in journal (Refereed)
    Abstract [en]

    GARD - Genomic Allergen Rapid Detection is a cell based alternative to animal testing for identification of skin sensitizers. The assay is based on a biomarker signature comprising 200 genes measured in an in vitro model of dendritic cells following chemical stimulations, and consistently reports predictive performances similar to 90% for classification of external test sets. Within the field of in vitro skin sensitization testing, definition of applicability domain is often neglected by test developers, and assays are often considered applicable across the entire chemical space. This study complements previous assessments of model performance with an estimate of confidence in individual classifications, as well as a statistically valid determination of the applicability domain for the GARD assay. Conformal prediction was implemented into current GARD protocols, and a large external test dataset (n = 70) was classified at a confidence level of 85%, to generate a valid model with a balanced accuracy of 88%, with none of the tested chemical reactivity domains identified as outside the applicability domain of the assay. In conclusion, results presented in this study complement previously reported predictive performances of GARD with a statistically valid assessment of uncertainty in each individual prediction, thus allowing for classification of skin sensitizers with confidence.

  • 8.
    Garcia de Lomana, Marina
    et al.
    BASF SE, Ludwigshafen am Rhein, Germany; Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Morger, Andrea
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
    Norinder, Ulf
    Örebro University, School of Science and Technology.
    Buesen, Roland
    BASF SE, Ludwigshafen am Rhein, Germany.
    Landsiedel, Robert
    BASF SE, Ludwigshafen am Rhein, Germany.
    Volkamer, Andrea
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
    Kirchmair, Johannes
    Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Mathea, Miriam
    BASF SE, Ludwigshafen am Rhein, Germany.
    ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities2021In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 61, no 7, p. 3255-3272Article in journal (Refereed)
    Abstract [en]

    Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.

  • 9.
    Groß, Alexander
    et al.
    Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
    Kracher, Barbara
    Institute of Biochemistry and Molecular Biology, Ulm University, Ulm, Germany.
    Kraus, Johann M.
    Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
    Kühlwein, Silke D.
    Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
    Pfister, Astrid S.
    Institute of Biochemistry and Molecular Biology, Ulm University, Ulm, Germany.
    Wiese, Sebastian
    Core Unit Mass Spectrometry and Proteomics, Ulm University, Ulm, Germany.
    Luckert, Katrin
    NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
    Pötz, Oliver
    NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
    Joos, Thomas
    NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
    Van Daele, Dries
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    De Raedt, Luc
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    Kühl, Michael
    Institute of Biochemistry and Molecular Biology, Ulm University, Ulm, Germany.
    Kestler, Hans A.
    Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
    Representing Dynamic Biological Networks With Multi-Scale Probabilistic Models2019In: Communications Biology, E-ISSN 2399-3642, Vol. 2, article id 21Article in journal (Refereed)
    Abstract [en]

    Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.

  • 10.
    Heuckeroth, Steffen
    et al.
    University of Münster, Münster, Germany.
    Damiani, Tito
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Smirnov, Aleksandr
    University of North Carolina at Charlotte, Charlotte, NC, USA.
    Mokshyna, Olena
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Brungs, Corinna
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Korf, Ansgar
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Smith, Joshua David
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic; First Faculty of Medicine, Charles University, Prague, Czech Republic.
    Stincone, Paolo
    University of Tuebingen, Tuebingen, Germany.
    Dreolin, Nicola
    Waters Corporation, Wilmslow, UK.
    Nothias, Louis-Félix
    University of Geneva, Geneva, Switzerland; Université Côte d'Azur, CNRS, ICN, Nice, France.
    Hyötyläinen, Tuulia
    Örebro University, School of Science and Technology.
    Oresic, Matej
    Örebro University, School of Medical Sciences. University of Turku and Åbo Akademi University, Turku, Finland.
    Karst, Uwe
    University of Münster, Münster, Germany.
    Dorrestein, Pieter C.
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
    Petras, Daniel
    University of Tuebingen, Tuebingen, Germany; University of California Riverside, Riverside, CA, USA.
    Du, Xiuxia
    University of North Carolina at Charlotte, Charlotte, NC, USA.
    van der Hooft, Justin J. J.
    Wageningen University & Research, Wageningen, the Netherlands; University of Johannesburg, Johannesburg, South Africa.
    Schmid, Robin
    University of Münster, Münster, Germany; Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
    Pluskal, Tomáš
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Reproducible mass spectrometry data processing and compound annotation in MZmine 32024In: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799Article, review/survey (Refereed)
    Abstract [en]

    Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

  • 11.
    Honma, Masamitsu
    et al.
    Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki Ku, Japan.
    Kitazawa, Airi
    Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki Ku, Japan.
    Cayley, Alex
    Lhasa Limited, Leeds, England.
    Williams, Richard V.
    Lhasa Limited, Leeds, England.
    Barber, Chris
    Lhasa Limited, Leeds, England.
    Hanser, Thierry
    Lhasa Limited, Leeds, England.
    Saiakhov, Roustem
    MultiCASE Inc., Beachwood, USA.
    Chakravarti, Suman
    MultiCASE Inc., Beachwood, USA.
    Myatt, Glenn J.
    Leadscope Inc., Columbus, USA.
    Cross, Kevin P.
    Leadscope Inc., Columbus, USA.
    Benfenati, Emilio
    Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
    Raitano, Giuseppa
    Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
    Mekenyan, Ovanes
    Laboratory of Mathematical Chemistry, As Zlatarov University, Bourgas, Bulgaria.
    Petkov, Petko
    Laboratory of Mathematical Chemistry, As Zlatarov University, Bourgas, Bulgaria.
    Bossa, Cecilia
    Istituto Superiore di Sanita', Rome, Italy.
    Benigni, Romualdo
    Istituto Superiore di Sanita', Rome, Italy; Alpha-Pretox, Rome, Italy.
    Battistelli, Chiara Laura
    Istituto Superiore di Sanita', Rome, Italy.
    Giuliani, Alessandro
    Istituto Superiore di Sanita', Rome, Italy.
    Tcheremenskaia, Olga
    Istituto Superiore di Sanita', Rome, Italy.
    DeMeo, Christine
    Prous Institute, Barcelona, Spain.
    Norinder, Ulf
    Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Koga, Hiromi
    Fujitsu Kyushu Systems Limited, Fukuoka, Japan.
    Jose, Ciloy
    Fujitsu Kyushu Systems Limited, Fukuoka, Japan.
    Jeliazkova, Nina
    IdeaConsult Ltd., Sofia, Bulgaria.
    Kochev, Nikolay
    IdeaConsult Ltd., Sofia, Bulgaria; Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria.
    Paskaleva, Vesselina
    Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria.
    Yang, Chihae
    Molecular Networks GmbH, Nürnberg, Germany; Altamira LLC, Columbus, USA.
    Daga, Pankaj R.
    Simulations Plus Inc., Lancaster, USA.
    Clark, Robert D.
    Simulations Plus Inc., Lancaster, USA.
    Rathman, James
    Molecular Networks GmbH, Nürnberg, Germany; Altamira LLC, Columbus, USA; Ohio State University, Columbus, USA.
    Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 3-16Article in journal (Refereed)
    Abstract [en]

    The International Conference on Harmonization (ICH) M7 guideline allows the use of in silicoapproaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis,National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

  • 12.
    Katajamaa, Mikko
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Miettinen, Jarkko
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    Turku Centre for Biotechnology, Turku, Finland.
    MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data2006In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 22, no 5, p. 634-636Article in journal (Refereed)
    Abstract [en]

    Summary: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis.

    Avalibility: MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.

  • 13.
    Kimmig, Angelika
    et al.
    Department of Computer Science, KU Leuven, Heverlee, Belgium.
    Van den Broeck, Guy
    Department of Computer Science, KU Leuven, Heverlee, Belgium.
    De Raedt, Luc
    Department of Computer Science, KU Leuven, Heverlee, Belgium.
    Algebraic Model Counting2017In: Journal of Applied Logic, ISSN 1570-8683, E-ISSN 1570-8691, Vol. 22, p. 46-62Article in journal (Refereed)
    Abstract [en]

    Weighted model counting (WMC) is a well-known inference task onknowledge bases, used for probabilistic inference in graphical models. Weintroduce algebraic model counting (AMC), a generalization of WMC toa semiring structure. We show that AMC generalizes many well-knowntasks in a variety of domains such as probabilistic inference, soft con-straints and network and database analysis. Furthermore, we investigateAMC from a knowledge compilation perspective and show that all AMCtasks can be evaluated usingsd-DNNFcircuits. We identify further char-acteristics of AMC instances that allow for the use of even more succinct circuits.

  • 14.
    Kråkström, Matilda
    et al.
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
    Dickens, Alex M
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
    Alves, Marina Amaral
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
    Forssten, Sofia D.
    Global Health and Nutrition Sciences, International Flavors & Fragrances, 02460 Kantvik, Finland.
    Ouwehand, Arthur C.
    Global Health and Nutrition Sciences, International Flavors & Fragrances, 02460 Kantvik, Finland.
    Hyötyläinen, Tuulia
    Örebro University, School of Science and Technology.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
    Lamichhane, Santosh
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
    Dynamics of the Lipidome in a Colon Simulator2023In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 13, no 3, article id 355Article in journal (Refereed)
    Abstract [en]

    Current evidence suggests that gut microbiome-derived lipids play a crucial role in the regulation of host lipid metabolism. However, not much is known about the dynamics of gut microbial lipids within the distinct gut biogeographic. Here we applied targeted and untargeted lipidomics to in vitro-derived feces. Simulated intestinal chyme was collected from in vitro gut vessels (V1-V4), representing proximal to distal parts of the colon after 24 and 48 h with/without polydextrose treatment. In total, 44 simulated chyme samples were collected from the in vitro colon simulator. Factor analysis showed that vessel and time had the strongest impact on the simulated intestinal chyme lipid profiles. We found that levels of phosphatidylcholines, sphingomyelins, triacylglycerols, and endocannabinoids were altered in at least one vessel (V1-V4) during simulation. We also found that concentrations of triacylglycerols, diacylglycerols, and endocannabinoids changed with time (24 vs. 48 h of simulation). Together, we found that the simulated intestinal chyme revealed a wide range of lipids that remained altered in different compartments of the human colon model over time.

  • 15.
    Lindh, Martin
    et al.
    Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden.
    Karlen, Anders
    Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institute, Unit of Toxicology Sciences, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework2017In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 14, no 5, p. 1571-1576Article in journal (Refereed)
    Abstract [en]

    Skin serves as a drug administration route, and skin permeability of chemicals is of significant interest in the pharmaceutical and cosmetic industries. An aggregated conformal prediction (ACP) framework was used to build models, for predicting the permeation rate (log K-p) of chemical compounds through human skin. The conformal prediction method gives as an output the prediction range at a given level of confidence for each compound, which enables the user to make a more informed decision when, for example, suggesting the next compound to prepare, Predictive models were built using;both the random forest and the support vector machine methods and were based on experimentally derived permeability data on 211 diverse compounds. The derived models were of similar predictive quality as compared to earlier published models but have the extra advantage of not only presenting a single predicted value for each, compound but also a reliable, individually assigned prediction range. The models use calculated descriptors and can quickly predict the skin permeation rate of new compounds.

  • 16.
    Lövfors, William
    et al.
    Örebro University, School of Medical Sciences. Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Mathematics, Linköping University, Linköping, Sweden; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
    Magnusson, Rasmus
    School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden.
    Jönsson, Cecilia
    Department of Biomedical Engineering, Linköping University, Linköping, Sweden. 2 Department of Mathematics, Linköping University, Linköping, Sweden; Department of Biomedical Engineering, Linköping University, Linköping, Sweden. 2 Department of Mathematics, Linköping University, Linköping, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Olofsson, Charlotta S.
    Department of Physiology/Metabolic Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
    Cedersund, Gunnar
    Örebro University, School of Medical Sciences. Department of Biomedical Engineering, Linköping University, Linköping, Sweden; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden .
    Nyman, Elin
    Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
    A comprehensive mechanistic model of adipocyte signaling with layers of confidence2023In: npj Systems Biology and Applications, E-ISSN 2056-7189, Vol. 9, no 1, article id 24Article in journal (Refereed)
    Abstract [en]

    Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

  • 17.
    Morger, Andrea
    et al.
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin, Berlin, Germany.
    Svensson, Fredrik
    Alzheimer's Research UK UCL Drug Discovery Institute, London, UK.
    Arvidsson McShane, Staffan
    Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Gauraha, Niharika
    Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Division of Computational Science and Technology, KTH, Stockholm, Sweden.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Dept. Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Spjuth, Ola
    Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Volkamer, Andrea
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin, Berlin, Germany.
    Assessing the calibration in toxicological in vitro models with conformal prediction2021In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 13, no 1, article id 35Article in journal (Refereed)
    Abstract [en]

    Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy-exchanging the calibration data only-is convenient as it does not require retraining of the underlying model.

  • 18.
    Neumann, Gunter
    et al.
    School of Medical Health (MV), Örebro University, Örebro, Sweden.
    Wall, Rebecca
    Örebro University, School of Medical Sciences.
    Rangel, Ignacio
    Örebro University, School of Medical Sciences.
    Marques, Tatiana M.
    Örebro University, School of Medical Sciences.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Qualitative modelling of the interplay of inflammatory status and butyrate in the human gut: a hypotheses about robust bi-stability2018In: BMC Systems Biology, E-ISSN 1752-0509, Vol. 12, no 1, article id 144Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Gut microbiota interacts with the human gut in multiple ways. Microbiota composition is altered in inflamed gut conditions. Likewise, certain microbial fermentation products as well as the lipopolysaccharides of the outer membrane are examples of microbial products with opposing influences on gut epithelium inflammation status. This system of intricate interactions is known to play a core role in human gut inflammatory diseases. Here, we present and analyse a simplified model of bidirectional interaction between the microbiota and the host: in focus is butyrate as an example for a bacterial fermentation product with anti-inflammatory properties.

    RESULTS: We build a dynamical model based on an existing model of inflammatory regulation in gut epithelial cells. Our model introduces both butyrate as a bacterial product which counteracts inflammation, as well as bacterial LPS as a pro-inflammatory bacterial product. Moreover, we propose an extension of this model that also includes a feedback interaction towards bacterial composition. The analysis of these dynamical models shows robust bi-stability driven by butyrate concentrations in the gut. The extended model hints towards a further possible enforcement of the observed bi-stability via alteration of gut bacterial composition. A theoretical perspective on the stability of the described switch-like character is discussed.

    CONCLUSIONS: Interpreting the results of this qualitative model allows formulating hypotheses about the switch-like character of inflammatory regulation in the gut epithelium, involving bacterial products as constitutive parts of the system. We also speculate about possible explanations for observed bimodal distributions in bacterial compositions in the human gut. The switch-like behaviour of the system proved to be mostly independent of parameter choices. Further implications of the qualitative character of our modeling approach for the robustness of the proposed hypotheses are discussed, as well as the pronounced role of butyrate compared to other inflammatory regulators, especially LPS, NF- κB and cytokines.

  • 19.
    Norinder, Ulf
    et al.
    Swetox, Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Ahlberg, Ernst
    AstraZeneca R&D Gothenburg, Mölndal, Sweden.
    Carlsson, Lars
    Computer Learning Research Centre, University of London Egham, Surrey, England.
    Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 33-40Article in journal (Refereed)
    Abstract [en]

    Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source. The former models only result in valid predictions for the majority, non-mutagenic, class whereas the latter models are valid for both classes, i.e. mutagenic and non-mutagenic compounds. These results demonstrate the importance of data consistency manifested through the superior predictive quality and validity of the models based only on DGM/NIHS generated data compared to a combination of this data with public data sources.

  • 20.
    Norinder, Ulf
    et al.
    Swedish Toxicology Sciences Research Center, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Rybacka, Aleksandra
    Department of Chemistry, Umeå University, Umeå, Sweden.
    Andersson, Patrik L.
    Department of Chemistry, Umeå University, Umeå, Sweden.
    Conformal prediction to define applicability domain: A case study on predicting ER and AR binding2016In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 27, no 4, p. 303-316Article in journal (Refereed)
    Abstract [en]

    A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.

  • 21.
    Norinder, Ulf
    et al.
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Spjuth, Ola
    Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Svensson, Fredrik
    The Alzheimer's Research UK University College London Drug Discovery Institute, London, U.K..
    Using Predicted Bioactivity Profiles to Improve Predictive Modeling2020In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 60, no 6, p. 2830-2837Article in journal (Refereed)
    Abstract [en]

    Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

  • 22.
    Oramas, Jose
    et al.
    KU Leuven, ESAT-PSI, IMEC, Heverlee, Belgium.
    De Raedt, Luc
    KU Leuven, CS-DTAI, Heverlee, Belgium.
    Tuytelaars, Tinne
    KU Leuven, ESAT-PSI, IMEC, Heverlee, Belgium.
    Context-based Object Viewpoint Estimation: A 2D Relational Approach2017In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 160, p. 100-113Article in journal (Refereed)
    Abstract [en]

    The task of object viewpoint estimation has been a challenge since the early days of computer vision. To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, extrinsic elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been proven to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for viewpoint estimation. In particular, we look at object configurations by following a relational neighbor-based approach for reasoning about object relations. We show that, starting from noisy object detections and viewpoint estimates, exploiting the estimated viewpoint and location of other objects in the scene can lead to improved object viewpoint predictions. Experiments on the KITTI dataset demonstrate that object configurations can indeed be used as a complementary cue to appearance-based viewpoint estimation. Our analysis reveals that the proposed context-based method can improve object viewpoint estimation by reducing specific types of viewpoint estimation errors commonly made by methods that only consider local information. Moreover, considering contextual information produces superior performance in scenes where a high number of object instances occur. Finally, our results suggest that, following a cautious relational neighbor formulation brings improvements over its aggressive counterpart for the task of object viewpoint estimation.

  • 23.
    Repsilber, Dirk
    et al.
    Institute for Biometry and Informatics, SLU, Uppsala.
    Kim, Jan T.
    Institut für Neuro- und Bioinformatik, Lübeck, Germany.
    Liljenström, Hans
    Institute for Biometry and Informatics, SLU, Uppsala.
    Martinetz, Thomas
    Institut für Neuro- und Bioinformatik, Lübeck, Germany.
    Using coarse-grained, discrete systems for data-driven inference of regulatory gene networks: Perspectives and limitations for reverse engineering2002In: Proceedings of the Fifth German Workshop on Artificial Life (GWAL2002) / [ed] Polani, D., Kim, J. T., Martinetz, T., 2002, p. 67-76Conference paper (Refereed)
  • 24.
    Repsilber, Dirk
    et al.
    Universitätsklinikum Schleswig-Holstein, Lübeck, Institut für Medizinische Biometrie und Statistik, Lübeck, Germany.
    Mira, A
    Lindroos, H
    Ziegler, A
    Andersson, Siv
    Reducing false positive rates by rotating microarray-based genomotyping data2004In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 46, p. 57-Article in journal (Refereed)
    Abstract [en]

    Microarray-based comparative genomic hybridization is leading to an increased understanding of bacterial evolution and patho-genesis by efficiently comparing whole genomes. Here, a sample genome is compared to a reference genome via comparative hybridization using two different fluorescent dyes. The logratio of the fluorescence intensities for sample and reference genome for each gene on the array is then usually compared to a cutoff classifying the belonging gene as absent or present with respect to the sample genome. The resulting list of candidate absent genes then undergoes confirmational analyses, i.e. PCR or sequencing, which are decisive with respect to both time and costs of the experiment. Thus, there is vital interest to reduce the rate of false positives in the list of candidate absent genes from the comparative genomic hybridization step. Our approach to accomplish this task uses the expected relationship between the logratio and the mean log intensities for absent genes to linear transform the inten-sity data before comparing to a cutoff. For validated data from a series of comparative genomic hybridizations for two Bartonella species we assessed significance and efficacy of the new approach. We show that we are able to halve the rate of false positives in the list of candidate absent genes for a typical comparative genomic hybridization experiment, thus significantly reducing time and costs of necessary confirmational analyses.

  • 25. Repsilber, Dirk
    et al.
    Selbig, Joachim
    Ziegler, Andreas
    Ensuring comparability for valid design and analysis of microarray gene expression experiments2006In: Clinical Chemistry and Laboratory Medicine, ISSN 1434-6621, E-ISSN 1437-4331, Vol. 44, no 6, p. A101-A102Article in journal (Refereed)
  • 26.
    Rush, Stephen
    et al.
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    Pinder, Shaun
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    Costa, Marcio
    Department of Pathobiology, University of Guelph, Guelph ON, Canada.
    Kim, Peter
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    A microbiology primer for pyrosequencing2012In: Quantitative Bio-Science, ISSN 2288-1344, Vol. 31, no 2, p. 53-81Article in journal (Refereed)
    Abstract [en]

    Metagenomic analysis is a very rich area for understanding the microbiology of organisms. Once the data has been assembled mathematical and statistical methods can be applied providing insights into biological properties that created the data in the first place. The foundations however, require some knowledge of microbiology which is not usually part of a mathematician’s nor a statistician’s training, and therefore, the data creation can itself be quite mysterious. In this paper we attempt to explain the microbiology to mathematicians and statisticians in a way that would hopefully provide insights into the data generating process. In particular our approach is specific to the open-source bioinformatics toolbox mothur. We will assume the reader has very little microbiology training but has some mathematical skills. It is the endeavor of this write-up to help bridge a needed gap.

  • 27.
    Salek, Reza M
    et al.
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Neumann, Steffen
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Schober, Daniel
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Hummel, Jan
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Billiau, Kenny
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Kopka, Joachim
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Correa, Elon
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Reijmers, Theo
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Rosato, Antonio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Tenori, Leonardo
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy; FiorGen Foundation, Sesto Fiorentin FI, Italy.
    Turano, Paola
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Marin, Silvia
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Deborde, Catherine
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Jacob, Daniel
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Rolin, Dominique
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Dartigues, Benjamin
    Centre of bioinformatics of Bordeaux (CBiB), University of Bordeaux, Bordeaux, France.
    Conesa, Pablo
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Haug, Kenneth
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Rocca-Serra, Philippe
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    O'Hagan, Steve
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Hao, Jie
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    van Vliet, Michael
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Sysi-Aho, Marko
    Zora Biosciences OY, Espoo, Finland.
    Ludwig, Christian
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Bouwman, Jildau
    Microbiology & Systems Biology TNO, Zeist, Netherlands.
    Cascante, Marta
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Ebbels, Timothy
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    Griffin, Julian L
    Medical Research Council Human Nutrition Research, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Moing, Annick
    INRA, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center (IBVM), Centre INRA Bordeaux Villenave d’Ornon, Univ. Bordeaux, Bordeaux, France.
    Nikolski, Macha
    University of Bordeaux CBiB/LaBRI, Bordeaux, France.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Zora Biosciences OY, Espoo, Finland.
    Sansone, Susanna-Assunta
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    Viant, Mark R.
    School of Biosciences, University of Birmingham Edgbaston, Birmingham, United Kingdom.
    Goodacre, Royston
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Günther, Ulrich L
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Hankemeier, Thomas
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Luchinat, Claudio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Walther, Dirk
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Steinbeck, Christoph
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access2015In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 11, no 6, p. 1587-1597Article in journal (Refereed)
    Abstract [en]

    Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

  • 28.
    Schmid, Robin
    et al.
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA; Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany; Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Heuckeroth, Steffen
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Korf, Ansgar
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Smirnov, Aleksandr
    Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
    Myers, Owen
    Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
    Dyrlund, Thomas S.
    Steno Diabetes Center Copenhagen, Gentofte, Denmark.
    Bushuiev, Roman
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Murray, Kevin J.
    Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
    Hoffmann, Nils
    Institute for Bio- and Geosciences (IBG-5), Forschungszentrum Jülich GmbH, Jülich, Germany.
    Lu, Miaoshan
    School of Engineering, Westlake University, Hangzhou, China.
    Sarvepalli, Abinesh
    BlockLab, Center for Large Datasystems Research, San Diego Supercomputer Center, La Jolla, CA, USA.
    Zhang, Zheng
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
    Fleischauer, Markus
    Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
    Dührkop, Kai
    Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
    Wesner, Mark
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Hoogstra, Shawn J.
    Agriculture and Agri-Food Canada, London Research and Development Centre, London, Ontario, Canada.
    Rudt, Edward
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Mokshyna, Olena
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Brungs, Corinna
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Ponomarov, Kirill
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Mutabdžija, Lana
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Damiani, Tito
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Pudney, Chris J.
    Datacraft Technologies, Mosman Park, Washington, Western Australia, Australia.
    Earll, Mark
    Analytical Solutions Group, Product Technology and Engineering, Jealott's Hill International Research Centre, Bracknell, UK.
    Helmer, Patrick O.
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Fallon, Timothy R.
    Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
    Schulze, Tobias
    Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
    Rivas-Ubach, Albert
    Ecology and Forest Genetics, Institute of Forest Sciences (ICIFOR-INIA-CSIC), Madrid, Spain.
    Bilbao, Aivett
    Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
    Richter, Henning
    Clinic for Diagnostic Imaging, Diagnostic Imaging Research Unit (DIRU), University of Zurich, Zürich, Switzerland.
    Nothias, Louis-Félix
    School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
    Wang, Mingxun
    Department of Computer Science, University of California Riverside, Riverside, CA, USA.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
    Weng, Jing-Ke
    Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
    Böcker, Sebastian
    Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
    Jeibmann, Astrid
    Institute of Neuropathology, University Hospital Münster, Münster, Germany.
    Hayen, Heiko
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Karst, Uwe
    Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
    Dorrestein, Pieter C.
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
    Petras, Daniel
    CMFI Cluster of Excellence, University of Tuebingen, Tuebingen, Germany.
    Du, Xiuxia
    Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
    Pluskal, Tomáš
    Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
    Integrative analysis of multimodal mass spectrometry data in MZmine 32023In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 41, no 4, p. 447-449Article in journal (Refereed)
  • 29.
    Sen, Partho
    et al.
    Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
    Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine2023In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 13, no 7, article id 855Article, review/survey (Refereed)
    Abstract [en]

    Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

  • 30.
    Sen, Partho
    et al.
    Örebro University, School of Medical Sciences. Örebro University Hospital. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland; .
    Oresic, Matej
    Örebro University, School of Medical Sciences. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
    Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview2019In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, no 2, article id E22Article, review/survey (Refereed)
    Abstract [en]

    There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

  • 31.
    Singh, Nitesh Kumar
    et al.
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Repsilber, Dirk
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Liebscher, Volkmar
    Institute for Mathematics and Informatics, Ernst Moritz Arndt University of Greifswald, Greifswald, Germany.
    Taher, Leila
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Identifying genes relevant to specific biological conditions in time course microarray experiments2013In: PLOS ONE, E-ISSN 1932-6203, Vol. 8, no 10, article id e76561Article in journal (Refereed)
    Abstract [en]

    Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call "relative Signal-to-Noise ratio" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

  • 32.
    Sun, Sun
    et al.
    Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden; Research Group Health Outcomes and Economic Evaluation, Department of Learning, Informatics, Management and Ethics, Karolinska Instiutet, Solna, Sweden.
    Stenberg, Erik
    Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Surgery.
    Cao, Yang
    Örebro University, School of Medical Sciences. Örebro University Hospital.
    Lindholm, Lars
    Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden.
    Salén, Klas-Göran
    Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden.
    Franklin, Karl A.
    Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden.
    Luo, Nan
    NUS Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
    Mapping the obesity problems scale to the SF-6D: results based on the Scandinavian Obesity Surgery Registry (SOReg)2023In: European Journal of Health Economics, ISSN 1618-7598, E-ISSN 1618-7601, Vol. 24, no 2, p. 279-292Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Obesity Problem Scale (OP) is a widely applied instrument for obesity, however currently calculation of health utility based on OP is not feasible as it is not a preference-based measure. Using data from the Scandinavian Obesity Surgery Registry (SOReg), we sought to develop a mapping algorithm to estimate SF-6D utility from OP. Furthermore, to test whether the mapping algorithm is robust to the effect of surgery.

    METHOD: The source data SOReg (n = 36 706) contains both OP and SF-36, collected at pre-surgery and at 1, 2 and 5 years post-surgery. The Ordinary Least Square (OLS), beta-regression and Tobit regression were used to predict the SF-6D utility for different time points respectively. Besides the main effect model, different combinations of patient characteristics (age, sex, Body Mass Index, obesity-related comorbidities) were tested. Both internal validation (split-sample validation) and validation with testing the mapping algorithm on a dataset from other time points were carried out. A multi-stage model selection process was used, accessing model consistency, parsimony, goodness-of-fit and predictive accuracy. Models with the best performance were selected as the final mapping algorithms.

    RESULTS: The final mapping algorithms were based on OP summary score using OLS models, for pre- and post-surgery respectively. Mapping algorithms with different combinations of patients' characteristics were presented, to satisfy the user with a different need.

    CONCLUSION: This study makes available algorithms enabling crosswalk from the Obesity Problem Scale to the SF-6D utility. Different mapping algorithms are recommended for the mapping of pre- and post-operative data.

  • 33.
    Telaar, Anna
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Biomarker discovery: classification using pooled samples2013In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 28, no 1, p. 67-106Article in journal (Refereed)
    Abstract [en]

    RNA-sample pooling is sometimes inevitable, but should be avoided in classification tasks like biomarker studies. Our simulation framework investigates a two-class classification study based on gene expression profiles to point out howstrong the outcomes of single sample designs differ to those of pooling designs. The results show how the effects of pooling depend on pool size, discriminating pattern, number of informative features and the statistical learning method used (support vector machines with linear and radial kernel, random forest (RF), linear discriminant analysis, powered partial least squares discriminant analysis (PPLS-DA) and partial least squares discriminant analysis (PLS-DA)). As a measure for the pooling effect, we consider prediction error (PE) and the coincidence of important feature sets for classification based on PLS-DA, PPLS-DAand RF. In general, PPLS-DAand PLS-DAshow constant PE with increasing pool size and low PE for patterns for which the convex hull of one class is not a cover of the other class. The coincidence of important feature sets is larger for PLS-DA and PPLS-DA as it is for RF. RF shows the best results for patterns in which the convex hull of one class is a cover of the other class, but these depend strongly on the pool size. We complete the PE results with experimental data whichwe pool artificially. The PE of PPLS-DAand PLS-DAare again least influenced by pooling and are low. Additionally, we show under which assumption the PLS-DA loading weights, as a measure for importance of features regarding classification, are equal for the different designs.

  • 34.
    Wilm, Anke
    et al.
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany; HITeC e.V., Hamburg, Germany.
    Garcia de Lomana, Marina
    Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Stork, Conrad
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany.
    Mathai, Neann
    Computational Biology Unit (CBU), Department of Chemistry, University of Bergen, Bergen, Norway.
    Hirte, Steffen
    Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Kühnl, Jochen
    Front End Innovation, Beiersdorf AG, Hamburg, Germany.
    Kirchmair, Johannes
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany; Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors2021In: Pharmaceuticals, E-ISSN 1424-8247, Vol. 14, no 8, article id 790Article in journal (Refereed)
    Abstract [en]

    In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.

  • 35.
    Wilm, Anke
    et al.
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany; HITeC e.V., Hamburg, Germany.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Agea, M. Isabel
    Department of Informatics and Chemistry, University of Chemistry and Technology Prague, Prague, Czech Republic.
    de Bruyn Kops, Christina
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany.
    Stork, Conrad
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany.
    Kühnl, Jochen
    Front End Innovation, Beiersdorf AG, Hamburg, Germany.
    Kirchmair, Johannes
    Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany; Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
    Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules2021In: Chemical Research in Toxicology, ISSN 0893-228X, E-ISSN 1520-5010, Vol. 34, no 2, p. 330-344Article in journal (Refereed)
    Abstract [en]

    Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/.

  • 36.
    Zhang, Jin
    et al.
    Department of Drug Metabolism and Pharmacokinetics, Janssen Pharmaceutica NV, Beerse, Belgium.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Svensson, Fredrik
    The Alzheimer's Research UK University College London Drug Discovery Institute, The Cruciform Building, London, U.K.
    Deep Learning-Based Conformal Prediction of Toxicity2021In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 61, no 6, p. 2648-2657Article in journal (Refereed)
    Abstract [en]

    Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.

  • 37.
    Zhang, Xueli
    et al.
    Örebro University, School of Medical Sciences. Centre for Systems Biology, Soochow University, Suzhou, China.
    Sun, Xiao-Feng
    Department of Oncology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
    Cao, Yang
    Örebro University, School of Medical Sciences. Örebro University Hospital. Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
    Ye, Benchen
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Peng, Qiliang
    Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
    Liu, Xingyun
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Shen, Bairong
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Zhang, Hong
    Örebro University, School of Medical Sciences.
    CBD: a biomarker database for colorectal cancer2018In: Database: The Journal of Biological Databases and Curation, E-ISSN 1758-0463, article id bay046Article in journal (Refereed)
    Abstract [en]

    Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.

  • 38. Ziegler, Andreas
    et al.
    Repsilber, Dirk
    Data rotation for efficient comparative genomics2004Conference paper (Refereed)
1 - 38 of 38
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