A novel method of predicting protein disordered regions based on sequence features

BioMed Research International

Volumn 2013, Issue , 2013, Pages

  Zhao, Tong Hui ^a   Jiang, Min ^b   Huang, Tao ^c   Li, Bi Qing ^d   Zhang, Ning ^e   Li, Hai Peng ^d   Cai, Yu Dong ^a  

^a SHANGHAI UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY SCHOOL OF MEDICINE   (China)

^c MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^d Chinese Academy of Sciences   (China)

^e TIANJIN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION COEFFICIENT; EXPERIMENTAL STUDY; INCREMENTAL FEATURE SELECTION; MACHINE LEARNING; MAXIMUM RELEVANCY MINIMUM REDUNDANCY; PRACTICE GUIDELINE; PREDICTIVE VALUE; PROMOTER REGION; PROTEIN ANALYSIS; PROTEIN DATA BANK; PROTEIN SECONDARY STRUCTURE; RANDOM FOREST; SEQUENCE ANALYSIS; STRUCTURE ACTIVITY RELATION; ALGORITHM; BIOLOGY; CHEMISTRY; POSITION WEIGHT MATRIX; PROTEIN TERTIARY STRUCTURE;

PROTEIN;

ALGORITHMS; COMPUTATIONAL BIOLOGY; POSITION-SPECIFIC SCORING MATRICES; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 84878157618     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2013/414327     Document Type: Article

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