You can view the full text of this article for using the link below.
Title: Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer
Authors: Sungmin Rhee; Sangsoo Lim; Sun Kim
Computer Science and Engineering, Seoul National University, Seoul, South Korea
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
Computer Science and Engineering, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
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.
Keywords: microRNA; regulatory network inference; optimisation; dynamic programming.
Int. J. of Data Mining and Bioinformatics, 2017 Vol.17, No.1, pp.25 - 41
Submission date: 18 Feb 2017
Date of acceptance: 21 Feb 2017
Available online: 02 May 2017