Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins

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doi doi:10.2390/biecoll-jib-2010-143
submission March 17, 2010
last revision July 07, 2010
published July 13, 2010
NCBI PubMed PubMed ID 20625199

V Mallika, K C Sivakumar, S Jaichand and EV Soniya

Correspondence should be addressed to:
EV Soniya
Plant Molecular Biology Division, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram - 695 014, Kerala, India
ni.ser.bcgr@nullayinosve


Abstract

Type III Polyketide synthases (PKS) are family of proteins considered to have significant roles in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins shows positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machines (SVMs). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. The PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.

Reference

V Mallika, KC Sivakumar, S Jaichand and EV Soniya. Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins. Journal of Integrative Bioinformatics, 7(1):143, 2010. Online Journal: http://journal.imbio.de/index.php?paper_id=143
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