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NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence
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instance of
scholarly article
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title
NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence
(English)
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author
Tim Hubbard
series ordinal
2
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author name string
Thomas A Down
series ordinal
1
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language of work or name
English
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publication date
2005
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published in
Nucleic Acids Research
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volume
33
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page(s)
1445-53
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issue
5
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cites work
The Ensembl genome database project
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stated in
PubMed Central
reference URL
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1064142
retrieved
7 April 2017
Sequence logos: a new way to display consensus sequences
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PubMed Central
reference URL
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7 April 2017
Fitting a mixture model by expectation maximization to discover motifs in biopolymers
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PubMed Central
reference URL
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7 April 2017
Assessing computational tools for the discovery of transcription factor binding sites
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7 April 2017
JASPAR: an open-access database for eukaryotic transcription factor binding profiles
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Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster.
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28 September 2017
Independent component analysis of microarray data in the study of endometrial cancer.
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28 September 2017
Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes
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Gibbs Recursive Sampler: finding transcription factor binding sites
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Additivity in protein-DNA interactions: how good an approximation is it?
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Mining for putative regulatory elements in the yeast genome using gene expression data
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Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites inE. coli
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Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences
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Over- and under-representation of short oligonucleotides in DNA sequences
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The hardwiring of development: organization and function of genomic regulatory systems.
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Identification of regulatory regions which confer muscle-specific gene expression.
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A probabilistic method to detect regulatory modules.
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A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling.
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reference URL
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Algorithms for extracting structured motifs using a suffix tree with an application to promoter and regulatory site consensus identification.
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28 November 2018
Identifiers
DOI
10.1093/NAR/GKI282
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PMCID
1064142
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PubMed ID
15760844
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ResearchGate publication ID
7973298
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