Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models.
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DeepChrome: deep-learning for predicting gene expression from histone modificationsHuman Transcriptome and Chromatin Modifications: An ENCODE PerspectiveModeling gene expression using chromatin features in various cellular contextsCombining transcription factor binding affinities with open-chromatin data for accurate gene expression predictionBayesian immunological model development from the literature: example investigation of recent thymic emigrantsRegulatorTrail: a web service for the identification of key transcriptional regulators.Differential peak calling of ChIP-seq signals with replicates with THORThe HTPmod Shiny application enables modeling and visualization of large-scale biological data
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P2860
Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models.
description
2011 nî lūn-bûn
@nan
2011 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
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
name
Predicting gene expression in ...... ties by linear mixture models.
@ast
Predicting gene expression in ...... ties by linear mixture models.
@en
type
label
Predicting gene expression in ...... ties by linear mixture models.
@ast
Predicting gene expression in ...... ties by linear mixture models.
@en
prefLabel
Predicting gene expression in ...... ties by linear mixture models.
@ast
Predicting gene expression in ...... ties by linear mixture models.
@en
P2093
P2860
P1433
P1476
Predicting gene expression in ...... ties by linear mixture models.
@en
P2093
Francisco de A T de Carvalho
Helge G Roider
Ivan G Costa
Thais G do Rego
P2860
P2888
P356
10.1186/1471-2105-12-S1-S29
P478
12 Suppl 1
P577
2011-02-15T00:00:00Z
P6179
1020505822