Improving the chances of successful protein structure determination with a random forest classifier

Acta Crystallographica Section D: Biological Crystallography

Volumn 70, Issue 3, 2014, Pages 627-635

  Jahandideh, Samad ^a,b   Jaroszewski, Lukasz ^a,b,c   Godzik, Adam ^a,b,c  

^a BURNHAM INSTITUTE   (United States)

^b Joint Center for Structural Genomics   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Machine learning methods; Structural genomics; Target selection; XtalPred

Indexed keywords

AMINO ACID; PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHEMICAL PHENOMENA; CHEMISTRY; CLASSIFICATION; COMPUTER SIMULATION; CRYSTALLIZATION; MACHINE-LEARNING METHODS; PREDICTIVE VALUE; RANDOMIZATION; STRUCTURAL GENOMICS; SUPPORT VECTOR MACHINE; TARGET SELECTION; X RAY CRYSTALLOGRAPHY; XTALPRED;

MACHINE-LEARNING METHODS; STRUCTURAL GENOMICS; TARGET SELECTION; XTALPRED;

ALGORITHMS; AMINO ACIDS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; CRYSTALLIZATION; CRYSTALLOGRAPHY, X-RAY; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PROTEINS; RANDOM ALLOCATION; SUPPORT VECTOR MACHINES;

EID: 84896792916     PISSN: 09074449     EISSN: 13990047     Source Type: Journal    
DOI: 10.1107/S1399004713032070     Document Type: Article

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