Learning interpretable SVMs for biological sequence classification

BMC Bioinformatics

Volumn 7, Issue SUPPL.1, 2006, Pages

  Rätsch, Gunnar ^a   Sonnenburg, Sören ^b   Schäfer, Christin ^b  

^a FRIEDRICH MIESCHER LABORATORY OF THE MAX PLANCK SOCIETY   (Germany)

^b FRAUNHOFER FIRST   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL INSIGHT; BIOLOGICAL SEQUENCES; BIOLOGICAL SIGNALS; CLASSIFICATION ACCURACY; DECISION FUNCTIONS; MULTIPLE KERNEL LEARNING; SPLICE SITE PREDICTION; SUPPORT VECTOR MACHINE (SVMS);

ALGORITHMS;

SUPPORT VECTOR MACHINES;

NUCLEOTIDE; OLIGOMER;

ARTICLE; CAENORHABDITIS ELEGANS; DNA SEQUENCE; EXON; FALSE POSITIVE RESULT; KERNEL METHOD; MOLECULAR BIOLOGY; NONHUMAN; PREDICTION; PROBLEM SOLVING; RELIABILITY; SEQUENCE ANALYSIS; STATISTICAL SIGNIFICANCE; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BINDING SITE; BIOLOGY; CLUSTER ANALYSIS; COMPUTER PROGRAM; PROCEDURES; PROTEIN MOTIF; SEQUENCE ALIGNMENT; STATISTICAL MODEL;

ALGORITHMS; AMINO ACID MOTIFS; ARTIFICIAL INTELLIGENCE; BINDING SITES; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 33947325162     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-S1-S9     Document Type: Article

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