GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning - Ersoz, Nur Sebnem; Bakir-Gungor, Burcu; Yousef, Malik (Frontiers in Genetics, 2023)
Identifying significant sets of genes that are up/down-regulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selections (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provide a deep understanding of the diseases. In order to address this problem, biological domain knowledge based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge based methods take the underlying biology into account; and they integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, biological process of the gene product. In this study we developed a tool named GeNetOntology which performs Gene Ontology-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring and Modelling (G-S-M) is used to identify significant gene ontology terms. GO information has been used as the grouping information, and this information has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype. Our approach has been tested on 11 different gene expression datasets; and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model. GeNetOntology will assist geneticists and scientists to identify a range of disease related genes and ontologies in transcriptomic data analysis; and it will also help medical doctors to design diagnosis platforms and to improve patient treatment plans.