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General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2010 Vol.2(3): 420-424 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2010.V2.179

Naïve Bayes Classifier for Classification of Plantand Animal miRNA

Bhasker Pant, Kumud Pant and K. R. Pardasani

Abstract—MicroRNAs (miRNA) are single-stranded RNA molecules of about 21–23 nucleotides in length. MicroRNAs (miRNAs) constitute a large family of non coding RNAs that function to regulate gene expression. Till today wet lab experiments have been used to classify the miRNA of plants and animals. The wet lab techniques are highly expensive, labour intensive and time consuming. Thus there arises a need for computational approach for classification of plants and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. In view of abovea machine learning models has been developed for classification of plant and animal miRNA using Naive Bayes classifier. The model has been tested on available data and it gives results with 85.71% accuracy.

Index Terms—Micro RNA, RNA interference, Naïve Bayes Classifier.

B. Pant is with Bioinformatics Department, MANIT, Bhopal, India (e-mail: pantbhaskar2@gmail.com).
K. Pant is with Bioinformatics Department, MANIT, Bhopal, India (e-mail: pant.kumud@gmail.com).
K. R. Pardasani is with Department of Mathematics, MANIT, Bhopal, India (email: kamalraj@gmail.com).

[PDF]

Cite: Bhasker Pant, Kumud Pant and K. R. Pardasani, "Naïve Bayes Classifier for Classification of Plant and Animal miRNA," International Journal of Computer Theory and Engineering vol. 2, no. 3, pp. 420-424, 2010.  

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