Systematic identification of yeast cell cycle transcription factors using multiple data sources.
about
Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data.Statistical significance of variables driving systematic variation in high-dimensional data.Identifying cooperative transcription factors in yeast using multiple data sourcesStatistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathwaysMissing value imputation for microarray data: a comprehensive comparison study and a web tool.Shrinkage regression-based methods for microarray missing value imputation.Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.Different Functional Gene Clusters in Yeast have Different Spatial Distributions of the Transcription Factor Binding Sites.Identification of yeast cell cycle regulated genes based on genomic features.A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeastYCRD: Yeast Combinatorial Regulation DatabaseState of the art in silico tools for the study of signaling pathways in cancer.MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.A novel statistical approach for identification of the master regulator transcription factor.CoopTFD: a repository for predicted yeast cooperative transcription factor pairsRoles of trans and cis variation in yeast intraspecies evolution of gene expression.Computational systems biology of the cell cycle.Periodic mRNA synthesis and degradation co-operate during cell cycle gene expressionA Computational Method for Identifying Yeast Cell Cycle Transcription Factors.Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors.Physical Exercise Modulates L-DOPA-Regulated Molecular Pathways in the MPTP Mouse Model of Parkinson's Disease.Comparison of targeted maximum likelihood and shrinkage estimators of parameters in gene networks.YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules.YARG: A repository for arsenic-related genes in yeast
P2860
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P2860
Systematic identification of yeast cell cycle transcription factors using multiple data sources.
description
2008 nî lūn-bûn
@nan
2008 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
name
Systematic identification of y ...... s using multiple data sources.
@ast
Systematic identification of y ...... s using multiple data sources.
@en
type
label
Systematic identification of y ...... s using multiple data sources.
@ast
Systematic identification of y ...... s using multiple data sources.
@en
prefLabel
Systematic identification of y ...... s using multiple data sources.
@ast
Systematic identification of y ...... s using multiple data sources.
@en
P2860
P356
P1433
P1476
Systematic identification of y ...... s using multiple data sources.
@en
P2093
Wei-Sheng Wu
P2860
P2888
P356
10.1186/1471-2105-9-522
P577
2008-12-05T00:00:00Z
P5875
P6179
1038700686