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

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, an d 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 g ene 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 di d 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 differen t 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 viahttps://github.com/ozgurcingiz/GNIAP.Graphic abstract
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research