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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan

4 Issues per year


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 7, Issue 1

Issues

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

V Mallika
  • Plant Molecular Biology Division, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram - 695 014, Kerala, India
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/ Kc Sivakumar
  • Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram - 695 014, Kerala, India
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/ S Jaichand
  • Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram - 695 014, Kerala, India
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/ Ev Soniya
  • Corresponding author
  • Plant Molecular Biology Division, Rajiv Gandhi Centre for Biotechnology, Thycaud P O, Poojappura, Thiruvananthapuram - 695 014, Kerala, India
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2010-143

Summary

Type III Polyketide synthases (PKS) are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show 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 Machine (SVM). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. 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.

About the article

Published Online: 2016-10-18

Published in Print: 2010-03-01


Citation Information: Journal of Integrative Bioinformatics, Volume 7, Issue 1, Pages 47–54, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2010-143.

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© 2010 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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