Machine learning for regulatory analysis and transcription factor target prediction in yeast
about
In silico regulatory analysis for exploring human disease progression.A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.LipocalinPred: a SVM-based method for prediction of lipocalins.Differences in local genomic context of bound and unbound motifs.Discriminating between HuR and TTP binding sites using the k-spectrum kernel method.Classifying transcription factor targets and discovering relevant biological features.DNA structural properties in the classification of genomic transcription regulation elements.Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.Landscape of transcriptional deregulation in lung cancer.
P2860
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P2860
Machine learning for regulatory analysis and transcription factor target prediction in yeast
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
2007年论文
@zh
2007年论文
@zh-cn
name
Machine learning for regulator ...... tor target prediction in yeast
@en
type
label
Machine learning for regulator ...... tor target prediction in yeast
@en
prefLabel
Machine learning for regulator ...... tor target prediction in yeast
@en
P2093
P2860
P1476
Machine learning for regulator ...... tor target prediction in yeast
@en
P2093
Charles Delisi
Dustin T Holloway
P2860
P2888
P356
10.1007/S11693-006-9003-3
P577
2007-03-01T00:00:00Z