MTar: a computational microRNA target prediction architecture for human transcriptome

Background: 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 ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Vinod Chandra, Reshmi Girijadevi, Achuthsankar S. Nair, Sreenadhan S. Pillai, Radhakrishna M. Pillai
• Keywords: BMCBI 2010, MiRNA Target Prediction, MiRNA Targets, Target MRNAs