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primary topic: BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.

BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.
type: AcademicArticle
type: Article
abstract: This paper describes a method for predicting a supersecondary structural motif, beta-hairpins, in a protein sequence. The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs. Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict beta-hairpins. An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input. The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs. The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN. The performances of the methods developed were assessed in a test case, where predicted secondary structure and surface accessibility were used instead of the observed structure. The highest accuracy achieved by the SVM based method in the test case was 77.9%. A maximum accuracy of 71.1% with Matthew's correlation coefficient of 0.41 in the test case was obtained on a dataset previously used by X. Cruz, E. G. Hutchinson, A. Shephard and J. M. Thornton (2002) Proc. Natl Acad. Sci. USA, 99, 11157-11162. The performance of the method was also evaluated on proteins used in the '6th community-wide experiment on the critical assessment of techniques for protein structure prediction (CASP6)'. Based on the algorithm described, a web server, BhairPred (http://www.imtech.res.in/raghava/bhairpred/), has been developed, which can be used to predict beta-hairpins in a protein using the SVM approach.
date: 2005-07-01
volume: 33
issue: Web Se
is part of: Nucleic acids research
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
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subject: QR Microbiology

BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (PDF)
type: Document
license: http://creativecommons.org/licenses/by/3.0/
has file: raghava2005.4.pdf
has part: raghava2005.4.pdf

Oxford University Press
type: Organization

Nucleic acids research
type: Collection
issn: 13624962

G.P.S. Raghava
type: Person
given name: G.P.S.
family name: Raghava

Manish Kumar
type: Person
given name: Manish
family name: Kumar

Navjot K Natt
type: Person
given name: Navjot K
family name: Natt

Manoj Bhasin
type: Person
given name: Manoj
family name: Bhasin

BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (Image (PNG))
type: Document
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BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (Image (PNG))
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BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (Image (PNG))
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BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (Image (PNG))
type: Document
is version of: BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (PDF)
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BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (Indexer Terms)
type: Document
is version of: BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. (PDF)
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primary topic: BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.

QR Microbiology
type: Concept