Prediction of functional class of the SARS coronavirus proteins by a statistical learning method

Journal of Proteome Research

Volumn 4, Issue 5, 2005, Pages 1855-1862

  Cai, C Z ^a,b   Han, L Y ^a   Chen, X ^c   Cao, Z W ^d   Chen, Y Z ^a,d  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b CHONGQING UNIVERSITY   (China)

^c ZHEJIANG UNIVERSITY   (China)

^d SHANGHAI CENTER FOR BIOINFORMATION TECHNOLOGY   (China)

Author keywords

Distantly related protein; Protein functional characterization; SARS Coronuvirus; Support vector machines; SVMProt

Indexed keywords

ABC TRANSPORTER; DISTANTLY RELATED PROTEIN; FUNCTIONAL GROUP; ION CHANNEL; LYASE; METHANOL; OXIDOREDUCTASE; STRUCTURAL PROTEIN; UNCLASSIFIED DRUG; VIRUS PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; CHANNEL GATING; CLASSIFICATION; COMPUTER PROGRAM; DRUG DEVELOPMENT; FUNCTIONAL CLASS; INTERNET; NONHUMAN; PREDICTION; PRESSURE WAVE; PRIORITY JOURNAL; PROTEIN FUNCTION; SARS CORONAVIRUS; SEQUENCE ANALYSIS; SEQUENCE DATABASE; STATISTICAL ANALYSIS; STATISTICAL LEARNING METHOD; SUPPORT VECTOR MACHINE; SVMPROT; VIRUS GENOME; VIRUS INFECTION; VOLTAGE GATED ION CHANNEL;

ADENOSINE TRIPHOSPHATE; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; GENOME, VIRAL; MODELS, STATISTICAL; OPEN READING FRAMES; PROTEOME; PROTEOMICS; SARS VIRUS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; VIRAL PROTEINS;

CORONAVIRUS; SARS CORONAVIRUS; SARS COV;

EID: 26844538082     PISSN: 15353893     EISSN: None     Source Type: Journal    
DOI: 10.1021/pr050110a     Document Type: Article

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