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Bioinformatics tools for discovery and evaluation of biomarkers: Applications in clinical assessment of cancer
Örebro universitet, Institutionen för hälsovetenskap och medicin.ORCID-id: 0000-0001-9242-4852
2016 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Örebro: Örebro university , 2016. , s. 75
Serie
Örebro Studies in Medicine, ISSN 1652-4063 ; 130
Nyckelord [en]
Algorithms, biomarkers, machine learning, classification, cancer, microRNA database, microRNA discovery, partial least squares
Nationell ämneskategori
Medicinsk bioteknologi
Forskningsämne
Biomedicin
Identifikatorer
URN: urn:nbn:se:oru:diva-47176ISBN: 978-91-7529-111-6 (tryckt)OAI: oai:DiVA.org:oru-47176DiVA, id: diva2:885890
Disputation
2016-02-03, Insikten (Portalen), Högskolan i Skövde, Kanikegränd 3 A, Skövde, 09:00 (Svenska)
Opponent
Handledare
Tillgänglig från: 2015-12-21 Skapad: 2015-12-21 Senast uppdaterad: 2017-10-17Bibliografiskt granskad
Delarbeten
1. Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
Öppna denna publikation i ny flik eller fönster >>Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
2015 (Engelska)Ingår i: International Journal of Data Mining and Bioinformatics, ISSN 1748-5673, Vol. 13, nr 4, s. 338-359Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Geneva, Switzerland: InderScience Publishers, 2015
Nyckelord
Ensemble classifier; GenoScan; Machine learning; miRNA discovery; miRNA prediction; Regression model; RNA structure prediction
Nationell ämneskategori
Annan medicinsk grundvetenskap
Forskningsämne
Biomedicin
Identifikatorer
urn:nbn:se:oru:diva-47260 (URN)10.1504/IJDMB.2015.072755 (DOI)000366135400002 ()26547983 (PubMedID)2-s2.0-84946741012 (Scopus ID)
Tillgänglig från: 2016-01-20 Skapad: 2015-12-29 Senast uppdaterad: 2018-01-10Bibliografiskt granskad
2. Classification of tumor samples from expression data using decision trunks
Öppna denna publikation i ny flik eller fönster >>Classification of tumor samples from expression data using decision trunks
2013 (Engelska)Ingår i: Cancer Informatics, ISSN 1176-9351, E-ISSN 1176-9351, nr 12, s. 53-66Artikel i tidskrift (Refereegranskat) 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.

Nyckelord
classification, machine learning, gene expression, biomarkers
Nationell ämneskategori
Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)
Forskningsämne
Biomedicin
Identifikatorer
urn:nbn:se:oru:diva-47262 (URN)10.4137/CIN.S10356 (DOI)23467331 (PubMedID)2-s2.0-84874202131 (Scopus ID)
Tillgänglig från: 2015-12-29 Skapad: 2015-12-29 Senast uppdaterad: 2017-12-01Bibliografiskt granskad
3. miREC: a database of miRNAs involved in the development of endometrial cancer
Öppna denna publikation i ny flik eller fönster >>miREC: a database of miRNAs involved in the development of endometrial cancer
Visa övriga...
(Engelska)Artikel i tidskrift (Refereegranskat) Submitted
Nationell ämneskategori
Cancer och onkologi
Forskningsämne
Biomedicin
Identifikatorer
urn:nbn:se:oru:diva-43411 (URN)
Tillgänglig från: 2015-03-06 Skapad: 2015-03-06 Senast uppdaterad: 2017-10-17Bibliografiskt granskad
4. Biomarker discovery and partial least squares-driven exploratory analysis of cancer expression data
Öppna denna publikation i ny flik eller fönster >>Biomarker discovery and partial least squares-driven exploratory analysis of cancer expression data
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Annan medicinsk grundvetenskap
Forskningsämne
Biomedicin
Identifikatorer
urn:nbn:se:oru:diva-47698 (URN)
Tillgänglig från: 2016-01-20 Skapad: 2016-01-20 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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Ulfenborg, Benjamin

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