about
WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Imputation for transcription factor binding predictions based on deep learningAn efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency.Predicting enhancer activity and variant impact using gkm-SVM.Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb.Local sequence features that influence AP-1 cis-regulatory activity.TAD-free analysis of architectural proteins and insulators.Comparative Analysis of Immune Cells Reveals a Conserved Regulatory Lexicon.The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants.Common α-globin variants modify hematologic and other clinical phenotypes in sickle cell trait and disease.Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution.Sequence based prediction of enhancer regions from DNA random walk
P2860
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P2860
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
gkmSVM: an R package for gapped-kmer SVM
@ast
gkmSVM: an R package for gapped-kmer SVM
@en
type
label
gkmSVM: an R package for gapped-kmer SVM
@ast
gkmSVM: an R package for gapped-kmer SVM
@en
prefLabel
gkmSVM: an R package for gapped-kmer SVM
@ast
gkmSVM: an R package for gapped-kmer SVM
@en
P2093
P2860
P50
P356
P1433
P1476
gkmSVM: an R package for gapped-kmer SVM
@en
P2093
Michael A Beer
Morteza Mohammad-Noori
Narges Ghareghani
P2860
P304
P356
10.1093/BIOINFORMATICS/BTW203
P407
P577
2016-04-19T00:00:00Z