Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models
about
gkmSVM: an R package for gapped-kmer SVMPredicting the recurrence of noncoding regulatory mutations in cancerRNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approachEpigenomic annotation of noncoding mutations identifies mutated pathways in primary liver cancerIdentification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networksA novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota.Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes
P2860
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P2860
Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Identification of High-Impact ...... Specific Random Forest Models
@ast
Identification of High-Impact ...... Specific Random Forest Models
@en
type
label
Identification of High-Impact ...... Specific Random Forest Models
@ast
Identification of High-Impact ...... Specific Random Forest Models
@en
prefLabel
Identification of High-Impact ...... Specific Random Forest Models
@ast
Identification of High-Impact ...... Specific Random Forest Models
@en
P2860
P50
P1476
Identification of High-Impact ...... Specific Random Forest Models
@en
P2093
Dmitry Svetlichnyy
Mark Fiers
P2860
P304
P356
10.1371/JOURNAL.PCBI.1004590
P577
2015-11-12T00:00:00Z