Prediction of miRNA in Human MHC that Encodes Different Immunological Functions Using Support Vector Machines
Archana Prabahar and Jeyakumar Natarajan
Affiliation: Data mining and Text mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore- 641046, India.
Keywords: microRNA, leave-one-out cross validation, major histocompatability complex, support vector machine.
MicroRNAs (miRNAs) are short non-coding RNAs known to be involved in the gene regulatory functions in
human. Major histocompatibility complex (MHC) located on the short arm of chromosome 6 remains as one of the most
important regions associated with several human diseases. The complex spans ~4 Mb and covers >120 expressed genes.
Gene expression at transcriptional and post transcriptional level is modulated by microRNA (miRNA) in collision with
sequence polymorphism and epigenetic factors. In this study, we aim to predict miRNA responsible for different
immunological functions and disorders in MHC region. Sequential and structural features of microRNAs were used for
the classification of miRNA and other non-coding RNA data. Support vector machine (SVM) classifier was used for
prediction and evaluated by jackknife validation technique. Overall accuracy was found to be 97.56% using leave-one-out
cross validation technique. These experimental results confirm that our classification method predicts immune related
miRNA with high accuracy.
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