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Trends in Bioinformatics
  Year: 2018 | Volume: 11 | Issue: 1 | Page No.: 17-24
DOI: 10.3923/tb.2018.17.24
PremipreD: Precursor miRNA Prediction by Support Vector Machine Approach
Sasti Gopal Das , Hirak Jyoti Chakraborty and Abhijit Datta

Background and Objective: Precursor microRNA expressions vary depending on their cellular environment and a large amount of genome segments can be folded in similar pseudo precursor’s microRNA hairpins like structure. Therefore, detection of true precursor microRNA in a genome is challenging task. The computational prediction of precursor MicroRNAs first distinguishes a large amount of similar folded hairpins like structure in genome sequence as a pseudo or true precursor miRNAs. However, researchers need to be improving methods for identification of precursor MicroRNA in a genomic sequence. Materials and Methods: In this computational method, supervised machine learning approach was used as a classifier for classifying the true precursor miRNAs using sequence and secondary structure information. Results: The support vector machine (SVM) classifier achieved accuracy (Q) of 96.28% for predicting true pre-miRNAs. Here, a new precursor miRNA identification tool-PremipreD was developed which performs better in comparison to existing tools, in terms of overall performance and specificity. Conclusion: The PremipreD algorithm reduces the number of false positive prediction rate by using effective Support vector machine methods.
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How to cite this article:

Sasti Gopal Das, Hirak Jyoti Chakraborty and Abhijit Datta, 2018. PremipreD: Precursor miRNA Prediction by Support Vector Machine Approach. Trends in Bioinformatics, 11: 17-24.

DOI: 10.3923/tb.2018.17.24






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