A monte carlo em algorithm for de novo motif discovery in biomolecular sequences

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 6, Issue 3, 2009, Pages 370-386

  Bi, Chengpeng ^a,b  

^a CHILDREN S MERCY HOSPITAL   (United States)

^b UNIVERSITY OF MISSOURI KANSAS CITY SCHOOL OF MEDICINE   (United States)

Author keywords

Expectation maximization (EM); Monte Carlo EM; Motif discovery; Multiple sequence alignment; Transcriptional regulation

Indexed keywords

EXPECTATION MAXIMIZATION (EM); MONTE CARLO EM; MOTIF DISCOVERY; MULTIPLE SEQUENCE ALIGNMENT; TRANSCRIPTIONAL REGULATION;

ALIGNMENT; COMPUTER SIMULATION LANGUAGES; COUNTING CIRCUITS; DNA SEQUENCES; LAWS AND LEGISLATION; MAXIMUM PRINCIPLE; MONTE CARLO METHODS; NUCLEIC ACIDS; OPTIMIZATION; PULSE MODULATION; PULSE WIDTH MODULATION;

CLUSTERING ALGORITHMS;

EUKARYOTA; PHYTOLACCACEAE; PROKARYOTA;

DNA; PROTEIN; TRANSCRIPTION FACTOR;

ALGORITHM; AMINO ACID SEQUENCE; ANIMAL; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; CONFORMATION; GENETIC DATABASE; METHODOLOGY; MOLECULAR GENETICS; MONTE CARLO METHOD; NUCLEOTIDE SEQUENCE; PROBABILITY; PROTEIN MOTIF; PROTEIN SECONDARY STRUCTURE; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID MOTIFS; AMINO ACID SEQUENCE; ANIMALS; BASE SEQUENCE; COMPUTER SIMULATION; DATABASES, GENETIC; DNA; MARKOV CHAINS; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; MONTE CARLO METHOD; NUCLEIC ACID CONFORMATION; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ANALYSIS; TRANSCRIPTION FACTORS;

EID: 68649104708     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2008.103     Document Type: Article

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