The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria

Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This...

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Published in:	Interdisciplinary sciences : computational life sciences Vol. 13; no. 3; pp. 500 - 510
Main Authors:	Cingiz, Mustafa Özgür, Biricik, Göksel, Diri, Banu
Format:	Journal Article
Language:	English
Published:	Singapore Springer Singapore 01.09.2021 Springer Nature B.V
Subjects:	Algorithms Biological analysis Biological effects Biomedical and Life Sciences Breast cancer Computational Biology/Bioinformatics Computational Science and Engineering Computer Appl. in Life Sciences Criteria Datasets DNA microarrays Gene expression Genes Health Sciences Life Sciences Mathematical and Computational Physics Medicine miRNA Molecular structure Original Research Article Performance evaluation Prostate Prostate cancer Ribonucleic acid RNA Statistics for Life Sciences Theoretical Theoretical and Computational Chemistry Topology Biological assessment of GCNs Topological analysis of GCNs Gene co-expression networks Gene co-expression network inference algorithms Literature data-based overlap analysis
ISSN:	1913-2751, 1867-1462, 1867-1462
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Abstract	Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP . Graphic abstract
AbstractList	Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP.Graphic abstract Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP .Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP . Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP . Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP . Graphic abstract
Author	Biricik, Göksel Diri, Banu Cingiz, Mustafa Özgür
Author_xml	– sequence: 1 givenname: Mustafa Özgür orcidid: 0000-0003-4469-1440 surname: Cingiz fullname: Cingiz, Mustafa Özgür email: mustafa.cingiz@btu.edu.tr organization: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University – sequence: 2 givenname: Göksel surname: Biricik fullname: Biricik, Göksel organization: Computer Engineering Department, Yildiz Technical University – sequence: 3 givenname: Banu surname: Diri fullname: Diri, Banu organization: Computer Engineering Department, Yildiz Technical University
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Snippet	Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The...
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SubjectTerms	Algorithms Biological analysis Biological effects Biomedical and Life Sciences Breast cancer Computational Biology/Bioinformatics Computational Science and Engineering Computer Appl. in Life Sciences Criteria Datasets DNA microarrays Gene expression Genes Health Sciences Life Sciences Mathematical and Computational Physics Medicine miRNA Molecular structure Original Research Article Performance evaluation Prostate Prostate cancer Ribonucleic acid RNA Statistics for Life Sciences Theoretical Theoretical and Computational Chemistry Topology
Title	The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria
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