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OJBTM
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
ABSTRACT
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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