about
An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets.A multidimensional integration analysis reveals potential bridging targets in the process of colorectal cancer liver metastasisBase-resolution methylation patterns accurately predict transcription factor bindings in vivo.dCaP: detecting differential binding events in multiple conditions and proteinsIdentifying differential transcription factor binding in ChIP-seq.Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type.SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.EpiCompare: An online tool to define and explore genomic regions with tissue or cell type-specific epigenomic features.Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges.Systematic Discovery of Chromatin-Bound Protein Complexes from ChIP-seq Datasets.Genome-wide prediction of DNase I hypersensitivity using gene expression.Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.A Hierarchical Framework for State-Space Matrix Inference and Clustering.
P2860
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P2860
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
2013年论文
@zh
2013年论文
@zh-cn
name
Differential principal component analysis of ChIP-seq
@en
type
label
Differential principal component analysis of ChIP-seq
@en
prefLabel
Differential principal component analysis of ChIP-seq
@en
P2093
P2860
P356
P1476
Differential principal component analysis of ChIP-seq
@en
P2093
P2860
P304
P356
10.1073/PNAS.1204398110
P407
P577
2013-04-08T00:00:00Z