A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.
about
Current bioinformatic approaches to identify DNase I hypersensitive sites and genomic footprints from DNase-seq dataAccurate inference of transcription factor binding from DNA sequence and chromatin accessibility dataProtein-DNA binding in high-resolutionMining precise cause and effect rules in large time series data of socio-economic indicatorsUsing DNase digestion data to accurately identify transcription factor binding sitesLearning protein-DNA interaction landscapes by integrating experimental data through computational models.Combining transcription factor binding affinities with open-chromatin data for accurate gene expression predictionHow to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.Chromatin accessibility: a window into the genome.DNase footprint signatures are dictated by factor dynamics and DNA sequencec-Myb Binding Sites in Haematopoietic Chromatin LandscapesSurvey of protein-DNA interactions in Aspergillus oryzae on a genomic scaleDeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.Pharmacogene regulatory elements: from discovery to applicationsDiscovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape.Identifying and mitigating bias in next-generation sequencing methods for chromatin biology.Genomic footprinting.DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.LRPPRC-mediated folding of the mitochondrial transcriptome.
P2860
Q21131247-9A797DC9-5A65-4446-A394-BCB0DE7FD8CBQ24614616-0C95C27E-65A7-4E3C-9CE4-C62014900E12Q28084330-36413774-1848-48D4-9495-E542C3E6A81EQ28595422-B70970AC-AD88-4AB4-BF9D-591036B2CEA9Q30591192-63D3BE24-D3CE-4BF3-A6FD-C1A29506A91DQ30833821-93C276F0-4FC1-4AE3-AE62-54C46C860F6AQ31145466-3C818926-F5E9-4A9F-AF5B-6E7BCBA6ED49Q31159691-1ED97E82-4404-44B6-88FC-9CCBD030E867Q34452000-1736E7D0-5A1E-4A96-92CB-B0446E7A4622Q34746845-6B1BA78A-3964-4E71-95DE-BAD45A45EBB4Q35711929-57335397-0FE6-4D60-883F-763C578E510EQ35786148-DADA9B27-0FE3-4C0E-886F-4712897C59E1Q36228906-9A1A6C4C-A867-4975-B747-204CD26E28A4Q36424755-AAA76C45-D8A4-4540-B8E4-6F29F6B914B6Q37633701-32AD37CD-FB8E-4282-AA7F-08DB0BCAC2C4Q38249872-77AE9722-2BC2-400B-A8F6-3D010230FD08Q38749638-89AE93B1-430C-432E-ABF0-190B2BE6FD46Q38765570-ACD5A29C-C566-4E93-8CA2-27A28B5720C8Q47163631-397D34FF-0459-4431-A08E-457A16886F6E
P2860
A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.
description
2010 nî lūn-bûn
@nan
2010 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
A dynamic Bayesian network for ...... olecule-based sequencing data.
@ast
A dynamic Bayesian network for ...... olecule-based sequencing data.
@en
type
label
A dynamic Bayesian network for ...... olecule-based sequencing data.
@ast
A dynamic Bayesian network for ...... olecule-based sequencing data.
@en
prefLabel
A dynamic Bayesian network for ...... olecule-based sequencing data.
@ast
A dynamic Bayesian network for ...... olecule-based sequencing data.
@en
P2093
P2860
P356
P1433
P1476
A dynamic Bayesian network for ...... olecule-based sequencing data.
@en
P2093
Jeff A Bilmes
William S Noble
Xiaoyu Chen
P2860
P304
P356
10.1093/BIOINFORMATICS/BTQ175
P407
P577
2010-06-01T00:00:00Z