Fitting a mixture model by expectation maximization to discover motifs in biopolymers

Proceedings of the 2nd International Conference on Intelligent Systems for Molecular Biology, ISMB 1994

Volumn , Issue , 1994, Pages 28-36

  Bailey, Timothy L ^a   Elkan, Charles ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; DNA SEQUENCES; MAXIMUM PRINCIPLE;

BAYES-OPTIMAL; EXPECTATION MAXIMIZATION; FINITE MIXTURE MODELING; MIXTURE MODELING; OPTIMAL CLASSIFIERS; PROTEIN SEQUENCES; TWO-COMPONENT;

CLASSIFICATION (OF INFORMATION);

BIOPOLYMER;

ALGORITHM; ANIMAL; ARTICLE; CHEMISTRY; HUMAN; SEQUENCE ANALYSIS; THEORETICAL MODEL;

ALGORITHMS; ANIMALS; BIOPOLYMERS; HUMANS; MODELS, THEORETICAL; SEQUENCE ANALYSIS;

EID: 0028685490     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

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2. Bailey, T. L., and Elkan, C. 1993. Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Technical Report CS93-302, Department of Computer Science, University of California, San Diego.
3. Bailey, T. L., and Elkan, C. 1994. Fitting a two-component mixture model by expectation maximization to discover motifs in biopolymers. Technical Report CS94-351, Department of Computer Science, University of California, San Diego.
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