Context-specific independence mixture modeling for positional weight matrices.
about
Partially-supervised protein subclass discovery with simultaneous annotation of functional residues.PyMix--the python mixture package--a tool for clustering of heterogeneous biological data.Tree-based position weight matrix approach to model transcription factor binding site profiles.Searching for transcription factor binding sites in vector spacesEukaryotic transcription factor binding sites--modeling and integrative search methods.Finding subtypes of transcription factor motif pairs with distinct regulatory rolesAdvances in analysis of transcriptional regulatory networks.Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.An extended set of PRDM1/BLIMP1 target genes links binding motif type to dynamic repression.Boltzmann-Enhanced Flexible Atom-Pair Kernel with Dynamic Dimension Reduction.
P2860
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P2860
Context-specific independence mixture modeling for positional weight matrices.
description
2006 nî lūn-bûn
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2006年の論文
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2006年学术文章
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2006年学术文章
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2006年学术文章
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2006年學術文章
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name
Context-specific independence mixture modeling for positional weight matrices.
@en
Context-specific independence mixture modeling for positional weight matrices.
@nl
type
label
Context-specific independence mixture modeling for positional weight matrices.
@en
Context-specific independence mixture modeling for positional weight matrices.
@nl
prefLabel
Context-specific independence mixture modeling for positional weight matrices.
@en
Context-specific independence mixture modeling for positional weight matrices.
@nl
P356
P1433
P1476
Context-specific independence mixture modeling for positional weight matrices.
@en
P2093
Benjamin Georgi
P304
P356
10.1093/BIOINFORMATICS/BTL249
P407
P577
2006-07-01T00:00:00Z