Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type II Diabetes
暂无分享,去创建一个
Lin Yuan | Zhan-Li Sun | Chun-Hou Zheng | Wen Sha | C. Zheng | Zhan-Li Sun | Lin Yuan | Wen Sha
[1] David Eisenberg,et al. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles , 2001, Nature Genetics.
[2] Antonio Reverter,et al. Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer , 2006, Bioinform..
[3] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[4] Kathleen Marchal,et al. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms , 2006, BMC Bioinformatics.
[5] S. Horvath,et al. Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.
[6] Sangsoo Kim,et al. Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .
[7] Simon Kasif,et al. Biological context networks: a mosaic view of the interactome , 2006, Molecular systems biology.
[8] Pierre Baldi,et al. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..
[9] D. Allison,et al. Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.
[10] Mario Medvedovic,et al. A semi-parametric Bayesian model for unsupervised differential co-expression analysis , 2010, BMC Bioinformatics.
[11] M. McCarthy,et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes , 2008, Nature Genetics.
[12] Michael Griffin,et al. Gene co-expression network topology provides a framework for molecular characterization of cellular state , 2004, Bioinform..
[13] D Haussler,et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[14] Guoping Fan,et al. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells , 2009, BMC Genomics.
[15] Hui Yu,et al. Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs , 2011, BMC Bioinformatics.
[16] R. Wilcox. Introduction to Robust Estimation and Hypothesis Testing , 1997 .
[17] John Quackenbush,et al. Genesis: cluster analysis of microarray data , 2002, Bioinform..
[18] G. Abecasis,et al. A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants , 2007, Science.
[19] Robert Clarke,et al. Differential dependency network analysis to identify condition-specific topological changes in biological networks , 2009, Bioinform..