Improving the Caenorhabditis elegans genome annotation using machine learning

PLoS Computational Biology

Volumn 3, Issue 2, 2007, Pages 0313-0322

  Rätsch, Gunnar ^a   Sonnenburg, Sören ^b   Srinivasan, Jagan ^c   Witte, Hanh ^d   Müller, Klaus R ^b,e   Sommer, Ralf J ^d   Schölkopf, Bernhard ^f  

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

^b FRAUNHOFER FIRST   (Germany)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d MAX PLANCK INSTITUTE FOR DEVELOPMENTAL BIOLOGY   (Germany)

^e TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^f MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LEARNING SYSTEMS;

CAENORHABDITIS ELEGANS; CODING REGION; ELEGANS; GENOME ANNOTATION; MACHINE LEARNING SYSTEMS; MACHINE-LEARNING; MODERN BIOLOGY; SEQUENCE LEARNING; STATE-OF-THE-ART MACHINE LEARNING METHODS; SUPPORT VECTORS MACHINE;

GENES;

MESSENGER RNA;

ACCURACY; ARTICLE; CAENORHABDITIS ELEGANS; EXON; GENETIC ANALYSIS; GENOME; HYPOTHESIS; INTRON; MACHINE LEARNING; NEMATODE; NONHUMAN; PROTOZOAL GENETICS; RETROSPECTIVE STUDY; RNA SPLICING; SUPPORT VECTOR MACHINE; UNTRANSLATED REGION; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHROMOSOME MAP; COMPARATIVE STUDY; DNA SEQUENCE; DOCUMENTATION; EVALUATION; GENETIC DATABASE; GENETICS; METHODOLOGY; MOLECULAR GENETICS; NUCLEOTIDE SEQUENCE; SEQUENCE ALIGNMENT;

CAENORHABDITIS ELEGANS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BASE SEQUENCE; CAENORHABDITIS ELEGANS; CHROMOSOME MAPPING; DATABASES, GENETIC; DOCUMENTATION; EXONS; INTRONS; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA;

EID: 33847268161     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.0030020     Document Type: Article

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