A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions

• Published in: Methods (San Diego, Calif.) Vol. 166; pp. 22 - 30
• Main Authors: Su, Lingtao, Liu, Guixia, Wang, Juexin, Xu, Dong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.08.2019
• Subjects: Algorithms; Biclustering; Biomarker; Breast cancer; breast neoplasms; Breast Neoplasms - genetics; Breast Neoplasms - pathology; breasts; Computational Biology; data collection; Female; gene expression regulation; Gene Expression Regulation, Neoplastic - genetics; Gene Regulatory Networks - genetics; Gene-miRNA interaction; genes; Humans; microRNA; MicroRNAs - genetics; miRNA; patients; protein-protein interactions; Rectified factor networks; RNA, Messenger - genetics; miRNA; Biomarker; Breast cancer; Biclustering; Rectified factor networks; Gene-miRNA interaction
• ISSN: 1046-2023; 1095-9130; 1095-9130
• DOI: 10.1016/j.ymeth.2019.05.010

•Biclustering analysis of integrated expression data from the same set of samples.•Identify breast cancer-specific biclusters with Rectified Factor Networks.•Identify breast cancer-related coding genes, microRNAs and their interactions.•Prioritize biomarkers by integrating multiple data sources and rank fusion process. Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein–protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.