Linked Life Data

18496789

Source:http://linkedlifedata.com/resource/pubmed/id/18496789

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-11-27
pubmed:abstractText
Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this article, we selected HIV-1 protease as the subject of the study. 299 oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. The peptides are represented by features constructed by AAIndex (Kawashima et al., Nucleic Acids Res 1999, 27, 368; Kawashima and Kanehisa, Nucleic Acids Res 2000, 28, 374). The mRMR method (Maximum Relevance, Minimum Redundancy; Ding and Peng, Proc Second IEEE Comput Syst Bioinformatics Conf 2003, 523; Peng et al., IEEE Trans Pattern Anal Mach Intell 2005, 27, 1226) combining with incremental feature selection (IFS) and feature forward search (FFS) are applied to find the two important cleavage sites and to select 364 important biochemistry features by jackknife test. Using KNN (K-nearest neighbors) to combine the selected features, the prediction model obtains high accuracy rate of 91.3% for Jackknife cross-validation test and 87.3% for independent-set test. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, especially for the scientists in this field.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1096-987X
pubmed:author
pubmed:copyrightInfo
Copyright 2008 Wiley Periodicals, Inc.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
33-9
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
HIV-1 protease cleavage site prediction based on amino acid property.
pubmed:affiliation
School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, People's Republic of China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't