Integrating multiple evidence sources to predict transcription factor binding in the human genome.
about
Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility dataEpigenetic priors for identifying active transcription factor binding sitesQuantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila developmentDREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression dataMotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis.Comparative annotation of functional regions in the human genome using epigenomic dataModels incorporating chromatin modification data identify functionally important p53 binding sitesInferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout dataIdentifying pathogenic processes by integrating microarray data with prior knowledgeA bayesian framework that integrates heterogeneous data for inferring gene regulatory networksSMARTS: reconstructing disease response networks from multiple individuals using time series gene expression dataInferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.PriorsEditor: a tool for the creation and use of positional priors in motif discovery.Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.Nucleosome organization in the vicinity of transcription factor binding sites in the human genome.LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression dataComputational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information.A ChIP-Seq benchmark shows that sequence conservation mainly improves detection of strong transcription factor binding sites.Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.The differential disease regulomeLarge scale comparison of innate responses to viral and bacterial pathogens in mouse and macaque.The E2F transcription factors regulate tumor development and metastasis in a mouse model of metastatic breast cancerDifferences in local genomic context of bound and unbound motifs.A wavelet approach to detect enriched regions and explore epigenomic landscapes.CBS: an open platform that integrates predictive methods and epigenetics information to characterize conserved regulatory features in multiple Drosophila genomesCTF: a CRF-based transcription factor binding sites finding system.The patterns of histone modifications in the vicinity of transcription factor binding sites in human lymphoblastoid cell lines.Interactions of chromatin context, binding site sequence content, and sequence evolution in stress-induced p53 occupancy and transactivation.Predicting tissue specific transcription factor binding sites.Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasisUnderstanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.Transcription factor binding sites prediction based on modified nucleosomesDecoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state.Multitask learning of signaling and regulatory networks with application to studying human response to fluThe role of chromatin accessibility in directing the widespread, overlapping patterns of Drosophila transcription factor binding.TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.A stationary wavelet entropy-based clustering approach accurately predicts gene expressionContribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.Uncovering MicroRNA Regulatory Hubs that Modulate Plasma Cell Differentiation
P2860
Q24614616-EE19FDD9-A798-4218-A293-32F0B1A55096Q24623379-A26A9FE2-CAC4-4EA5-A543-3130245AB6FCQ28477071-D37B700F-BB56-400E-AA90-91DF1127DE50Q30557846-C172D55D-B2D7-484B-AB74-AB86421D2FC0Q30586232-AF84185D-1DC9-4397-94AF-6244099AD31BQ30597594-CF0EA41E-57EF-4F79-9859-9AB8A15C0662Q30618656-BBF6F6BC-E668-4B75-96D8-6B70B455894EQ30763615-D20B47DD-3FED-46E1-A90E-1C36745425CAQ30807414-235F87B6-AA36-44B4-A64D-46668C6DE33DQ30844584-E1186FE1-B4DB-4D5C-B117-D839C10B961BQ30874976-404F4EAD-1769-4EAF-AEF5-4096ABC8F46AQ30936220-FE81E216-ACE4-4727-AC19-30C15B924323Q33632045-450B6FC6-DA19-4D23-BB7B-8F5DE3BD31BDQ33642588-57C1A2F7-001D-493C-AF59-1983E8091DF0Q33815124-6DD09779-50E0-4E30-BCDA-2C5B362EC7D0Q33851077-1C5763A9-F28C-4772-A10C-F9993A2CFEE8Q33860446-B9919FE6-5607-46FB-9BC2-210C6DE25B1EQ33886721-3FA75E20-9B6A-4452-ADDE-190C889347EAQ33939100-5A60AE0C-8254-4AE3-94EB-89E1B61628AAQ33953951-96128E34-36E5-4466-83F1-CF1808BA36CCQ33971466-2750DF25-F435-483F-B29F-983BDFEC06E2Q34056427-4356AC95-E705-435C-B3FC-688C1AF0E2C7Q34301632-28DF7B9B-CE52-4592-BF05-17D5F1AD6CB4Q34471128-9753BA36-9F29-41A5-B678-CCB99BCE4A73Q34507907-763A8027-1583-42BD-BAB4-BE0EC6E2CE68Q34530550-319A080E-300B-419C-840D-3CA064604FCDQ34635618-19AAC226-4258-4E1A-8F88-4FCF230AF0CFQ34873579-A6B5460D-75EA-4381-8B74-EF4B8F6E44D1Q35044479-C131D998-C375-4FC6-BC7F-D0E87971DA80Q35059602-D36CC212-BA4D-43E7-941C-F5FF1127B3B7Q35067178-C8F8F1FF-7221-469A-834B-855BDCABD030Q35106887-C7E7A7CD-EC7A-4FCA-B826-D49B6D7A275EQ35487251-F6F3F249-CCE8-492B-923F-6B090DB3A5A5Q35529844-0C9BC029-F715-4673-A63F-80289122603DQ35557755-0E32B122-CB24-4C88-85F3-CDE9DB2297C6Q35571086-0085A2C0-749E-478C-A677-483854D83D5AQ35641733-E212CE35-AC8E-4200-A1D6-57E1A6D968EEQ35751160-FE7ED0F1-59A3-4EB3-9759-7A981E42A4D5Q35823685-E76C5495-D9C1-4A2D-B5A0-DB1D8895BAFDQ35865929-AEDC50A1-B7B2-4300-B637-0A48E7F9AEAB
P2860
Integrating multiple evidence sources to predict transcription factor binding in the human genome.
description
2010 nî lūn-bûn
@nan
2010 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մարտին 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
Integrating multiple evidence ...... r binding in the human genome.
@ast
Integrating multiple evidence ...... r binding in the human genome.
@en
type
label
Integrating multiple evidence ...... r binding in the human genome.
@ast
Integrating multiple evidence ...... r binding in the human genome.
@en
prefLabel
Integrating multiple evidence ...... r binding in the human genome.
@ast
Integrating multiple evidence ...... r binding in the human genome.
@en
P2860
P356
P1433
P1476
Integrating multiple evidence ...... r binding in the human genome.
@en
P2093
Heather L Plasterer
Jason Ernst
P2860
P304
P356
10.1101/GR.096305.109
P577
2010-03-10T00:00:00Z