Vinod, Chandra and Reshmi , Girijadevi and Pillai, M R (2010) MTar: a computational microRNA target prediction architecture for human transcriptome. BMC bioinformatics, 11. ISSN 1471-2105

Text MTar A computational microRNA(BMC Bioinformatics).pdf Restricted to Registered users only Download (1148Kb) | Request a copy

Abstract

MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs. Results We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone. Conclusion MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.

Item Type:	Article
Subjects:	Cancer Research
Depositing User:	Central Library RGCB
Date Deposited:	24 May 2019 06:20
Last Modified:	24 May 2019 06:20
URI:	http://rgcb.sciencecentral.in/id/eprint/763

Actions (login required)

View Item