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primary topic: Prediction of nuclear proteins using SVM and HMM models.

Prediction of nuclear proteins using SVM and HMM models.
type: AcademicArticle
type: Article
abstract: This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.
date: 2009
volume: 10
is part of: BMC bioinformatics
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Prediction of nuclear proteins using SVM and HMM models. (PDF)
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has document: Prediction of nuclear proteins using SVM and HMM models. (Indexer Terms)
subject: Biotechnology

Prediction of nuclear proteins using SVM and HMM models. (PDF)
type: Document
license: http://creativecommons.org/licenses/by/3.0/
has file: raghava09.1.pdf
has part: raghava09.1.pdf

BIomedcentral
type: Organization

BMC bioinformatics
type: Collection
issn: 14712105

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

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

Prediction of nuclear proteins using SVM and HMM models. (Image (PNG))
type: Document
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Prediction of nuclear proteins using SVM and HMM models. (Image (PNG))
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Prediction of nuclear proteins using SVM and HMM models. (Image (PNG))
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Prediction of nuclear proteins using SVM and HMM models. (Image (PNG))
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Prediction of nuclear proteins using SVM and HMM models. (Indexer Terms)
type: Document
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primary topic: Prediction of nuclear proteins using SVM and HMM models.

Biotechnology
type: Concept
preferred label: QH301 Biology