TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.
about
Genome-Wide Identification of the Target Genes of AP2-O, a Plasmodium AP2-Family Transcription FactorArchitecture of the human regulatory network derived from ENCODE dataUnderstanding transcriptional regulation by integrative analysis of transcription factor binding data.Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencodersTarget analysis by integration of transcriptome and ChIP-seq data with BETA.Assessing computational methods for transcription factor target gene identification based on ChIP-seq data.DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding databPeaks: a bioinformatics tool to detect transcription factor binding sites from ChIPseq data in yeasts and other organisms with small genomes.OrthoClust: an orthology-based network framework for clustering data across multiple species.Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data.Comparative analysis of regulatory information and circuits across distant species.Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes.Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAs.REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.E2F4 regulatory program predicts patient survival prognosis in breast cancer.Phenotypic robustness and the assortativity signature of human transcription factor networks.An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulomeiTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data.Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the RegulomeAn algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data.E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder CancerChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.A probabilistic approach to learn chromatin architecture and accurate inference of the NF-κB/RelA regulatory network using ChIP-Seq.Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic ValueDemystifying the secret mission of enhancers: linking distal regulatory elements to target genes.Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation.Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAsCancer cell line specific co-factors modulate the FOXM1 cistrome.Systematic target function annotation of human transcription factors.
P2860
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P2860
TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@ast
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@en
type
label
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@ast
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@en
prefLabel
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@ast
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@en
P2860
P356
P1433
P1476
TIP: a probabilistic method fo ...... rom ChIP-seq binding profiles.
@en
P2093
Chao Cheng
Renqiang Min
P2860
P304
P356
10.1093/BIOINFORMATICS/BTR552
P407
P577
2011-10-29T00:00:00Z