GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning
Introduction: Identifying significant sets of genes that are up/downregulated 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 selection (i.e., gene selectio...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1139082/full |
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| author | Nur Sebnem Ersoz Burcu Bakir-Gungor Burcu Bakir-Gungor Malik Yousef Malik Yousef |
| author_facet | Nur Sebnem Ersoz Burcu Bakir-Gungor Burcu Bakir-Gungor Malik Yousef Malik Yousef |
| author_sort | Nur Sebnem Ersoz |
| collection | DOAJ |
| description | Introduction: Identifying significant sets of genes that are up/downregulated 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 selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of 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 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, and biological process of the gene product.Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which 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.Results: 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.Discussion: 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 doctors design diagnosis platforms and improve patient treatment plans. |
| first_indexed | 2024-03-12T14:03:35Z |
| format | Article |
| id | doaj.art-467206241eb14a6ca427aa874724d596 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-12T14:03:35Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-467206241eb14a6ca427aa874724d5962023-08-21T17:14:27ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-08-011410.3389/fgene.2023.11390821139082GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learningNur Sebnem Ersoz0Burcu Bakir-Gungor1Burcu Bakir-Gungor2Malik Yousef3Malik Yousef4Department of Bioengineering, Graduate School of Engineering and Science, Abdullah Gul University, Kayseri, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, TürkiyeDepartment of Bioengineering, Faculty of Life and Natural Sciences, Abdullah Gul University, Kayseri, TürkiyeDepartment of Information Systems, Zefat Academic College, Zefat, IsraelGalilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, IsraelIntroduction: Identifying significant sets of genes that are up/downregulated 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 selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of 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 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, and biological process of the gene product.Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which 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.Results: 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.Discussion: 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 doctors design diagnosis platforms and improve patient treatment plans.https://www.frontiersin.org/articles/10.3389/fgene.2023.1139082/fullgene ontologygene expression data analysismachine learningfeature selectionenrichment analysisfeature scoring |
| spellingShingle | Nur Sebnem Ersoz Burcu Bakir-Gungor Burcu Bakir-Gungor Malik Yousef Malik Yousef GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning Frontiers in Genetics gene ontology gene expression data analysis machine learning feature selection enrichment analysis feature scoring |
| title | GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning |
| title_full | GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning |
| title_fullStr | GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning |
| title_full_unstemmed | GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning |
| title_short | GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning |
| title_sort | genetontology identifying affected gene ontology terms via grouping scoring and modeling of gene expression data utilizing biological knowledge based machine learning |
| topic | gene ontology gene expression data analysis machine learning feature selection enrichment analysis feature scoring |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1139082/full |
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