Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.
about
InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites.Significance evaluation in factor graphs.Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors.Fast motif matching revisited: high-order PWMs, SNPs and indels.Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape.JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework.Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding.HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.Direct AUC optimization of regulatory motifs.The BaMM web server for de-novo motif discovery and regulatory sequence analysis.
P2860
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P2860
Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.
description
2016 nî lūn-bûn
@nan
2016年の論文
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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
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name
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@en
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@nl
type
label
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@en
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@nl
prefLabel
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@en
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@nl
P2860
P356
P1476
Bayesian Markov models consist ...... otifs in nucleotide sequences.
@en
P2093
Matthias Siebert
P2860
P304
P356
10.1093/NAR/GKW521
P407
P577
2016-06-09T00:00:00Z