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Online Journal of Bioinformatics ©
Volume 12(1):9-17, 2011
Prediction of mutagenicity of compounds by Support Vector Machine
Anju Sharma1,2*, Rajnish Kumar1,2, Pritish Varadwaj1
1Department of Bioinformatics, Indian Institute of Information Technology Allahabad,Deoghat, Jhalwa, Allahabad-211012, Uttar Pradesh, India. 2Amity Institute of Biotechnology (AIB), Amity University Uttar Pradesh (AUUP), Lucknow-206010, Uttar Pradesh, India
Sharma A, Kumar R, Varadwaj P. Prediction of mutagenicity of compounds by Support Vector Machine, Onl J Bioinform., 12(1):9-17, 2011. Various computational methods have been developed for mutagenicity prediction for in-vitro or in-vivo toxicity prediction. Radial Basis Function (RBF) kernel based Support Vector Machine (SVM) classification model was used for the prediction of mutagenicity using 17 physicochemical descriptors. The selection of optimal hyperplane parameters were performed with 1696 training compound data and the prediction efficiency of proposed classifier were tested on remaining 566 test data. The overall prediction efficiency was, 71.73%. Youden’s index and Matthew correlation index were found to be 0.43 and 0.43 respectively and the Area under Receiver Operating Curve (ROC) was found to be 0.7847. The overall performance of the model was equivalent to other reported methods.
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