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Title |
SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes
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Authors |
Koel Mukherjee, Abhipriya, Ambarish Saran Vidyarthi & Dev Mani Pandey*
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Affiliation |
Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi-835 215, Jharkhand, India |
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dmpandey@bitmesra.ac.in; *Corresponding author |
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Article Type |
Hypothesis |
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Date |
Received May 14, 2013; Accepted May 17, 2013; Published June 08, 2013
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Abstract |
Support vector machine is a class of machine learning algorithms which uses a set of related supervised learning methods for classification and regression. Nowadays this method is vividly applied to many detection problems related with secondary structure, tumor cell and binding residue prediction. In this work, support vector machines (SVMs) have been trained on 90 sequences of transcription factors with HTH motif. Four sequence features were used as attribute for the prediction of interaction site in HTH motif. A web page was also developed so that user can easily enter the protein sequence and receive the output as interaction site predicted or not predicted. The generated model shows a very high amount of accuracy, sensitivity and specificity which proves to be a good model for the selected case. |
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Keywords |
Support vector machine, machine learning algorithm, confusion matrix, helix turn helix motif.
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Citation |
Mukherjee et al.
Bioinformation 9(10): 500-505 (2013) |
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Edited by |
P Kangueane
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ISSN |
0973-2063
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Publisher |
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License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |