A machine learning approach for identifying novel cell type-specific transcriptional regulators of myogenesis.
about
What does our genome encode?Two Forkhead transcription factors regulate cardiac progenitor specification by controlling the expression of receptors of the fibroblast growth factor and Wnt signaling pathwaysPrediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approachImogene: identification of motifs and cis-regulatory modules underlying gene co-regulation.Genome-wide analysis of functional and evolutionary features of tele-enhancers.Highly parallel assays of tissue-specific enhancers in whole Drosophila embryosIntegrative analysis of the zinc finger transcription factor Lame duck in the Drosophila myogenic gene regulatory networkGenome-wide screens for in vivo Tinman binding sites identify cardiac enhancers with diverse functional architectures.RFECS: a random-forest based algorithm for enhancer identification from chromatin state.Contribution of distinct homeodomain DNA binding specificities to Drosophila embryonic mesodermal cell-specific gene expression programs.Enhancer modeling uncovers transcriptional signatures of individual cardiac cell states in Drosophila.High-resolution genome-wide DNA methylation maps of mouse primary female dermal fibroblasts and keratinocytes.Muscle cell fate choice requires the T-box transcription factor midline in DrosophilaIntegrating diverse datasets improves developmental enhancer predictionExtrapolating histone marks across developmental stages, tissues, and species: an enhancer prediction case studyHigh resolution mapping of enhancer-promoter interactions.An Orthologous Epigenetic Gene Expression Signature Derived from Differentiating Embryonic Stem Cells Identifies Regulators of CardiogenesisDifferential regulation of mesodermal gene expression by Drosophila cell type-specific Forkhead transcription factors.LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.Quantitative multivariate analysis of dynamic multicellular morphogenic trajectoriesCis-regulatory architecture of a brain signaling center predates the origin of chordates.Machine learning classification of cell-specific cardiac enhancers uncovers developmental subnetworks regulating progenitor cell division and cell fate specification.The myogenic repressor gene Holes in muscles is a direct transcriptional target of Twist and Tinman in the Drosophila embryonic mesoderm.A new method for enhancer prediction based on deep belief network.
P2860
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P2860
A machine learning approach for identifying novel cell type-specific transcriptional regulators of myogenesis.
description
2012 nî lūn-bûn
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2012 թուականի Մարտին հրատարակուած գիտական յօդուած
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2012 թվականի մարտին հրատարակված գիտական հոդված
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2012年の論文
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2012年論文
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2012年論文
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2012年論文
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2012年論文
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2012年論文
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2012年论文
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name
A machine learning approach fo ...... onal regulators of myogenesis.
@ast
A machine learning approach fo ...... onal regulators of myogenesis.
@en
A machine learning approach fo ...... onal regulators of myogenesis.
@nl
type
label
A machine learning approach fo ...... onal regulators of myogenesis.
@ast
A machine learning approach fo ...... onal regulators of myogenesis.
@en
A machine learning approach fo ...... onal regulators of myogenesis.
@nl
prefLabel
A machine learning approach fo ...... onal regulators of myogenesis.
@ast
A machine learning approach fo ...... onal regulators of myogenesis.
@en
A machine learning approach fo ...... onal regulators of myogenesis.
@nl
P2093
P2860
P1433
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A machine learning approach fo ...... onal regulators of myogenesis.
@en
P2093
Alan M Michelson
Brian W Busser
Ivan Ovcharenko
Leila Taher
Molly J Bloom
Terese Tansey
Yongsok Kim
P2860
P304
P356
10.1371/JOURNAL.PGEN.1002531
P577
2012-03-08T00:00:00Z