Predicting metal-binding sites from protein sequence

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 9, Issue 1, 2012, Pages 203-213

  Passerini, Andrea ^a   Lippi, Marco ^b   Frasconi, Paolo ^c  

^a UNIVERSITY OF TRENTO   (Italy)

^b UNIVERSITY OF SIENA   (Italy)

^c UNIVERSITY OF FLORENCE   (Italy)

Author keywords

greedy algorithms; machine learning; Metal binding prediction; structured output learning

Indexed keywords

3D INFORMATION; ACCURACY ESTIMATION; ALGEBRAIC STRUCTURES; ALGORITHMIC IDEAS; BONDING STATE; GENERAL GRAPH; GREEDY ALGORITHMS; MACHINE-LEARNING; METAL BINDING; METAL-BINDING SITES; PREDICTION PROBLEM; PROTEIN CHAINS; PROTEIN SEQUENCES; STRUCTURED-OUTPUT LEARNING; TRAINING SETS;

ALGORITHMS; AMINO ACIDS; FORECASTING; METAL IONS; PROTEINS; TRANSITION METALS;

BINDING SITES;

METAL; PROTEIN;

AMINO ACID SEQUENCE; BINDING SITE; BIOLOGY; CHEMISTRY; METABOLISM; MOLECULAR GENETICS; PROCEDURES; PROTEIN DATABASE; SEQUENCE ANALYSIS; ARTICLE; METHODOLOGY;

AMINO ACID SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; METALS; MOLECULAR SEQUENCE DATA; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 81455132670     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2011.94     Document Type: Article

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