Protein subcellular multi-localization prediction using a min-max modular support vector machine

International Journal of Neural Systems

Volumn 20, Issue 1, 2010, Pages 13-28

  Yang, Yang ^a   Lu, Bao Liang ^b,c,d  

^a SHANGHAI MARITIME UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c Chinese National Human Genome Center at Shanghai   (China)

^d MICROSOFT   (United States)

Author keywords

Min max modular; Protein subcellular multi localization; Support vector machine

Indexed keywords

AMINO ACID COMPOSITIONS; AUTOMATIC PREDICTION; BIOLOGICAL PROCESS; COMPUTATIONAL BIOLOGY; CRUCIAL FUNCTIONS; DECOMPOSITION METHODS; ENSEMBLE CLASSIFIERS; FEATURE VECTORS; GENE ONTOLOGY; GENERAL PATTERNS; LOCALIZATION PROBLEMS; MIN-MAX; MIN-MAX MODULAR SUPPORT VECTOR MACHINE; MODULAR PROTEINS; PREDICTION ACCURACY; PROTEIN DATA; PROTEIN FUNCTIONS; PROTEIN SEQUENCES; PROTEIN SUBCELLULAR LOCALIZATION; SECONDARY STRUCTURES; SEMANTIC INFORMATION; SOLVENT ACCESSIBILITY; SUB-CELLULAR; SUBCELLULAR LOCALIZATIONS; SUBCELLULAR LOCATION;

AMINO ACIDS; BIOINFORMATICS; BIOLOGY; CLASSIFIERS; FORECASTING; LOCATION; ONTOLOGY; ORGANIC ACIDS; SUPPORT VECTOR MACHINES; VECTORS;

PROTEINS;

PROTEIN;

ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; CLASSIFICATION; HUMAN; METHODOLOGY; PHYSIOLOGY; PREDICTION AND FORECASTING; PROTEIN DATABASE; PROTEIN QUATERNARY STRUCTURE; SEQUENCE ANALYSIS; TIME;

ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; HUMANS; MODELS, MOLECULAR; PATTERN RECOGNITION, AUTOMATED; PREDICTIVE VALUE OF TESTS; PROTEIN STRUCTURE, QUATERNARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; TIME FACTORS;

EID: 77951560201     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065710002206     Document Type: Article

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