Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer. (2017)

• Record Type: Journal Article
• Authors: Rhee, Sungmin; Lim, Sangsoo; Kim, Sun
• Is Part Of: International journal of data mining and bioinformatics. Volume 17:Number 1(2017)
• Page Start: 25
• Page End: 41
• Publication Date: 2017
• Subjects: microRNA -- regulatory network inference -- optimisation -- dynamic programming; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

MicroRNAs (miRNAs) have significant biological roles at the molecular level by regulating genes post-transcriptionally. To understand the functional effects of miRNAs in different biological contexts, it is essential to elucidate miRNA-mRNA regulatory modules (MRMs). The computational complexity for inferencing MRMs is very high due to the many-to-many relationships between miRNAs and mRNAs and inferencing MRMs is still a challenging unresolved problem. In this paper, we propose a novel iterative segmented least square method for functional MRM discovery. Our method operates in two steps: (a) grouping and ordering the miRNAs and mRNAs to build per-sample matrices representing miRNA-mRNA regulations, and (b) determining maximum sized modules from structured miRNA-mRNA matrices. In experiments with human breast cancer data sets from TCGA, we show that our method outperforms existing methods in terms of both GO similarity and cluster evaluation. In addition, we show that modules determined by our method can be used for breast cancer survival prediction and subtype classification.