Authors:
Nur Sebnem Ersoz
1
;
Burcu Bakir-Gungor
2
;
3
and
Malik Yousef
4
;
5
Affiliations:
1
Department of Bioengineering, Graduate School of Engineering and Science, Abdullah Gul University, Kayseri, Turkey
;
2
Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, 38080, Turkey
;
3
Department of Bioengineering, Faculty of Life and Natural Sciences, Abdullah Gul University, Kayseri, Turkey
;
4
Department of Information Systems, Zefat Academic College, Zefat, Israel
;
5
Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
Keyword(s):
Inflammatory Bowel Disease, Transcriptomic Data Analysis, Machine Learning, Grouping Based Feature Selection.
Abstract:
Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and affected gene ontology (GO) terms. In this study, via exploiting GeNetOntology method, we have reanalysed a gene expression data including Crohn’s Disease (CD) and Ulcerative colitis (UC) patients and controls. In order to identify IBD related genes and affected GO terms, GeNetOntology uses GO hierarchy as the biological domain knowledge while performing gene expression data analysis based on machine learning (ML). In the training part of GeNetOntology, genes annotated with selected ontology terms have been utilized to perform a two-class classification task which generates an important set of ontologies as an output. IBD data samples were obtained from peripheral blood and colon tissue. In order to investigate the effect of different collection sites, IBD data have been a
nalysed under different scenarios; i.e., all samples, only tissue samples and only blood samples. Experimental findings indicate that GeNetOntology can successfully determine significant disease-related ontology terms. Performance of the model slightly differs according to the sample source. Via analysing the differences/commonalities between affected gene ontologies under different scenarios, we attempt to enlighten IBD development mechanisms.
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