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State of the art prediction of HIV-1 protease cleavage sites
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Uppsala University, Uppsala, Sweden.
2015 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, no 8, p. 1204-1210Article in journal (Refereed) Published
Abstract [en]

Motivation: Understanding the substrate specificity of HIV-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

Results: The linear support vector machine with orthogonal encod-ing is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor ser-vices. It is also found that schemes using physicochemical proper-ties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.

Availability: The data sets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. © 2014 The Author.

Place, publisher, year, edition, pages
Oxford: Oxford University Press, 2015. Vol. 31, no 8, p. 1204-1210
Keywords [en]
Bioinformatics, HIV-1
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:hh:diva-27165DOI: 10.1093/bioinformatics/btu810ISI: 000354453700007PubMedID: 25504647Scopus ID: 2-s2.0-84927720595OAI: oai:DiVA.org:hh-27165DiVA, id: diva2:768706
Available from: 2014-12-04 Created: 2014-12-04 Last updated: 2018-03-22Bibliographically approved

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Rögnvaldsson, ThorsteinnYou, Liwen

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