Protein secondary structure prediction using three neural networks and a segmental semi Markov model

Mathematical Biosciences

Volumn 217, Issue 2, 2009, Pages 145-150

  Malekpour, Seyed Amir ^a,b   Naghizadeh, Sima ^c   Pezeshk, Hamid ^a   Sadeghi, Mehdi ^d   Eslahchi, Changiz ^e  

^a Center of Excellence in Nuclear Engineering   (Iran)

^b INSTITUTE FOR STUDIES IN THEORETICAL PHYSICS AND MATHEMATICS IPM   (Iran)

^c TARBIAT MODARES UNIVERSITY   (Iran)

^d NATIONAL INSTITUTE OF GENETIC ENGINEERING AND BIOTECHNOLOGY   (Iran)

^e SHAHID BEHESHTI UNIVERSITY   (Iran)

Author keywords

Bayesian methods; Conditional dependency models; Dependency window; Discrimination; Hidden Markov models (HMMs); Multi class pattern classification

Indexed keywords

BAYESIAN METHODS; CONDITIONAL DEPENDENCY MODELS; DEPENDENCY WINDOW; DISCRIMINATION; HIDDEN MARKOV MODELS (HMMS); MULTI-CLASS PATTERN CLASSIFICATION;

AMINES; AMINO ACIDS; BAYESIAN NETWORKS; BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL GRAMMARS; FUNCTIONS; HAND TOOLS; HIDDEN MARKOV MODELS; MARKOV PROCESSES; OBJECT RECOGNITION; ORGANIC ACIDS; PROBABILITY DENSITY FUNCTION; THREE DIMENSIONAL; WINDOWS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; MARKOV CHAIN; MODELING; PROTEIN;

MARKOV CHAINS; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ALIGNMENT;

EID: 58649083223     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mbs.2008.11.001     Document Type: Article

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