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BayesMD: Flexible Biological Modeling for Motif Discovery
Man-Hung Eric Tang,
Anders Krogh
,
Ole Winther
Functional Genomics
5
Citations (Scopus)
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Dive into the research topics of 'BayesMD: Flexible Biological Modeling for Motif Discovery'. Together they form a unique fingerprint.
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Keyphrases
Advanced Sampling
50%
Artificial Data
50%
Background Prior
50%
Biological Modeling
100%
Dirichlet
50%
Discovery Model
50%
Local Sequence
50%
Marginal Probability
100%
Maximum a Posteriori Inference
50%
Motif Discovery
100%
Multinomial
50%
Nucleosome Occupancy
50%
Number of Occurrences
50%
Parallel Tempering
50%
Post-analysis
50%
Prediction Data
50%
Software Model
50%
Transcription Factor Database
50%
Typical Properties
50%
Computer Science
Artificial Data
50%
Marginal Probability
100%
Priori Knowledge
50%
Social Exclusion
50%
Software Model
50%
Mathematics
Marginal Probability
100%
Marginalization
50%
Priori Knowledge
50%
Step Analysis
50%
Earth and Planetary Sciences
Sampling
100%
Social Exclusion
100%
Biochemistry, Genetics and Molecular Biology
Biological Modeling
100%
Parallel Tempering
33%