Journal of Proteomics & Bioinformatics
Open Access

Vinod Chandra SS

Publications

• Research Article
  HTar: Hidden Markov Model Based MicroRNA Binding Site Prediction
  Author(s): Salim A and Vinod Chandra SSSalim A and Vinod Chandra SS
  
  MicroRNAs are small, non-coding RNA molecules that regulate gene expression. MicroRNA may binds to mRNAs and control the intended function of mRNAs. There are a handful of computational algorithms for target prediction, but the degree of false positives and false negatives are high. In this paper, we propose a Hidden Markov Model for seed prediction and a Support Vector Machine (SVM) classifier for target prediction. Positive data set for training has been collected from experimentally validated targets, while negative data set has been identified systematically from predicted false positives. Each mRNA target candidate sequence is aligned with microRNA sequence and tested for a seed region using the trained HMM model. If the test succeeds, 22 features were extracted from the aligned duplex and fed into an SVM classifier. HMM based seed identification module works with an accuracy of .. View More»
  DOI: 10.4172/jpb.1000422

  Abstract PDF

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