Identifying microRNAs involved in cancer pathway using support vector machines. (April 2015)

• Record Type: Journal Article
• Authors: Kothandan, Ram; Biswas, Sumit
• Is Part Of: Computational biology and chemistry. Volume 55(2015)
• Page Start: 31
• Page End: 36
• Publication Date: 2015-04
• Subjects: Signatures -- miRNA:mRNA interaction -- Machine based learning -- Feature selection; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.01.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Construction of a two-step SVM classifier for identifying miRNA associated with cancer. Features are extracted from sequence, thermodynamics and miRNA–mRNA hybridization interactions based on experimentally data. For miRSEQ – Positions 1, 6, 10, 19, GG and CC repeat in the miRNA sequence form the optimal feature subset. Optimal features vary significantly based on the number of seed formed by hybrid for miRINT. Final classifier obtained a good performance with cv-rate ranging from 92 to 87. Abstract: Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification – 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA–mRNA structure. The two step classifier model – miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew's correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools ).