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Bioinformatics tools for discovery and evaluation of biomarkers: Applications in clinical assessment of cancer
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.ORCID iD: 0000-0001-9242-4852
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. A biomarker is a biological molecule found in tissues or body fluids and can be used to predict or assess disease states. The aim of this thesis is to develop bioinformatics tools for discovery and evaluation of novel biomarkers from high-throughput datasets.

MicroRNAs (miRNAs) are short non-coding RNAs that function as negative regulators of gene expression. Dysregulation of miRNAs in cancer is frequently reported, making them interesting as biomarkercandidates.  GenoScan was developed for genome-wide discovery of miRNA-coding genes, as a first step in the identification of novel mi-RNA biomarkers.

High-throughput technologies such as microarrays allow researchers to measure the expression of thousands of genes or miRNAs simultaneously. The Decision Trunk Classifier (DTC) algorithm has been developed to screen datasets from these experiments for biomarker candidates. When applied to a miRNA expression dataset for endometrial cancer (EC) samples vs. controls, a two-marker model with 98 % accuracy was generated. These miRNAs (hsa-miR-183-5p and hsamiRPlus-C1070) are promising as biomarkers for EC screening.

The miREC database was developed to store gene and miRNA data from curated expression profiling studies of EC, as well as gene-miRNA regulatory connections. Using gene-miRNA interaction networks from miREC, the roles of miRNAs in cancer hallmark acquisition can be clarified. To further support exploratory analysis of expression data, DTC was extended with partial least squares regression models. The resulting PLS-DTC algorithm can be used to gain deeper insights into the perturbation of biological processes and pathways.

Place, publisher, year, edition, pages
Örebro: Örebro university , 2016. , 75 p.
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 130
Keyword [en]
Algorithms, biomarkers, machine learning, classification, cancer, microRNA database, microRNA discovery, partial least squares
National Category
Medical Biotechnology
Research subject
Biomedicine
Identifiers
URN: urn:nbn:se:oru:diva-47176ISBN: 978-91-7529-111-6 (print)OAI: oai:DiVA.org:oru-47176DiVA: diva2:885890
Public defence
2016-02-03, Insikten (Portalen), Högskolan i Skövde, Kanikegränd 3 A, Skövde, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2015-12-21 Created: 2015-12-21 Last updated: 2017-10-17Bibliographically approved
List of papers
1. Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
Open this publication in new window or tab >>Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
2015 (English)In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5673, Vol. 13, no 4, 338-359 p.Article in journal (Refereed) Published
Abstract [en]

In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensemble-based and one using logistic regression were benchmarked along with 15 published methods. In addition, the sequence-folding step is addressed by investigating the impact of secondary structure prediction methods and the choice of input sequence length on prediction performance. Both the accuracy of secondary structure predictions and the miRNA prediction are evaluated. In the benchmark of hairpin classification methods, the regression model achieved highest classification accuracy. Of the structure prediction methods evaluated, ContextFold achieved the highest agreement between predicted and experimentally determined structures. However, both the choice of secondary structure prediction method and input sequence length had limited impact on hairpin classification performance.

Place, publisher, year, edition, pages
Geneva, Switzerland: InderScience Publishers, 2015
Keyword
Ensemble classifier; GenoScan; Machine learning; miRNA discovery; miRNA prediction; Regression model; RNA structure prediction
National Category
Other Basic Medicine
Research subject
Biomedicine
Identifiers
urn:nbn:se:oru:diva-47260 (URN)10.1504/IJDMB.2015.072755 (DOI)000366135400002 ()26547983 (PubMedID)2-s2.0-84946741012 (Scopus ID)
Available from: 2016-01-20 Created: 2015-12-29 Last updated: 2017-10-18Bibliographically approved
2. Classification of tumor samples from expression data using decision trunks
Open this publication in new window or tab >>Classification of tumor samples from expression data using decision trunks
2013 (English)In: Cancer Informatics, ISSN 1176-9351, E-ISSN 1176-9351, no 12, 53-66 p.Article in journal (Refereed) Published
Abstract [en]

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks," since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2-3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices.

Keyword
classification, machine learning, gene expression, biomarkers
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Research subject
Biomedicine
Identifiers
urn:nbn:se:oru:diva-47262 (URN)10.4137/CIN.S10356 (DOI)23467331 (PubMedID)2-s2.0-84874202131 (Scopus ID)
Available from: 2015-12-29 Created: 2015-12-29 Last updated: 2017-10-17Bibliographically approved
3. miREC: a database of miRNAs involved in the development of endometrial cancer
Open this publication in new window or tab >>miREC: a database of miRNAs involved in the development of endometrial cancer
Show others...
(English)Article in journal (Refereed) Submitted
National Category
Cancer and Oncology
Research subject
Biomedicine
Identifiers
urn:nbn:se:oru:diva-43411 (URN)
Available from: 2015-03-06 Created: 2015-03-06 Last updated: 2017-10-17Bibliographically approved
4. Biomarker discovery and partial least squares-driven exploratory analysis of cancer expression data
Open this publication in new window or tab >>Biomarker discovery and partial least squares-driven exploratory analysis of cancer expression data
(English)Manuscript (preprint) (Other academic)
National Category
Other Basic Medicine
Research subject
Biomedicine
Identifiers
urn:nbn:se:oru:diva-47698 (URN)
Available from: 2016-01-20 Created: 2016-01-20 Last updated: 2017-10-17Bibliographically approved

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