ENZPRED-Enzymatic Protein Class Predicting by Machine Learning

Author(s): Kirtan Dave, Hetalkumar Panchal.

Journal Name: Current Topics in Medicinal Chemistry

Volume 13 , Issue 14 , 2013

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Abstract:

Recent times have seen flooding of biological data into the scientific community. Due to increase in large amounts of data from genome and other sequencing projects become available, being diverted on to Insilco approach for data collection and prediction has become a priority also progresses in sequencing technologies have found an exponential function rise in the number of newly found enzymes. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. As new approaches are needed to determine the functions of the proteins these genes encode. The protein parameters that can be used for an enzyme/ non-enzyme classification includes features of sequences like amino acid composition, dipeptide composition, grand Average of hydropathicity (GRAVY), probability of being in alpha helix, probability of being in beta sheet Probability of being in a turn. We show how large-scale computational analysis can help to address this challenge by help of java and support vector machine library. In this paper, a recently developed machine learning algorithm referred to as the svm library Learning Machine is used to classify protein sequences with six main classes of enzyme data downloaded from a public domain database. Comparative studies on different type of kernel methods like 1.radial basis function, 2.polynomial available in SVM library. Results show that RBF method take less time in training and give more accurate result then other kernel methods to also less training time compared to other kernel methods. The classification accuracy of RBF is also higher than various methods in respect of available sequences data.

Keywords: Enzyme class, protein, support vector machine, kernel, java, functional class, model.

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Article Details

VOLUME: 13
ISSUE: 14
Year: 2013
Page: [1674 - 1680]
Pages: 7
DOI: 10.2174/15680266113139990118
Price: $58

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