Multi-level learning: Improving the prediction of protein, domain and residue interactions by allowing information flow between levels

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Yip, Kevin Y ^a   Kim, Philip M ^b   McDermott, Drew ^a   Gerstein, Mark ^a,c  

^a Yale University   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c YALE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BI-DIRECTIONAL FLOWS; COMPLEMENTARY FEATURES; CONCEPT HIERARCHIES; DIFFERENT GRANULARITIES; EXPLICIT INFORMATION; INTERACTION NETWORKS; LEARNING FRAMEWORKS; PHYSICAL-CHEMICAL FEATURES;

FORECASTING; LEARNING ALGORITHMS;

PROTEINS;

AMINO ACID; PROTEIN;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ANALYTICAL ERROR; ARTICLE; CONCEPTUAL FRAMEWORK; INFORMATION PROCESSING; KERNEL METHOD; MACHINE LEARNING; PREDICTION; PROTEIN ANALYSIS; PROTEIN DOMAIN; PROTEIN INTERACTION; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; GENETICS; METABOLISM; METHODOLOGY; PROTEIN MULTIMERIZATION; PROTEIN QUATERNARY STRUCTURE; PROTEIN TERTIARY STRUCTURE; SACCHAROMYCES CEREVISIAE;

COMPUTATIONAL BIOLOGY; MODELS, MOLECULAR; PROTEIN MULTIMERIZATION; PROTEIN STRUCTURE, QUATERNARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; SACCHAROMYCES CEREVISIAE;

EID: 69549108297     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-241     Document Type: Article

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