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» Gene Expression Clustering with Functional Mixture Models
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BMCBI
2010
109views more  BMCBI 2010»
13 years 10 months ago
Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression
Background: In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a comple...
Xiaojiang Xu, Stephen Hoang, Marty W. Mayo, Stefan...
BMCBI
2010
216views more  BMCBI 2010»
13 years 5 months ago
Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies
Background: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors f...
Bo Chen, Minhua Chen, John William Paisley, Aimee ...
BMCBI
2007
114views more  BMCBI 2007»
13 years 10 months ago
Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
Background: Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As ...
Xiaohua Hu, Fang-Xiang Wu
RECOMB
2010
Springer
14 years 4 months ago
Hierarchical Generative Biclustering for MicroRNA Expression Analysis
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie ...
José Caldas, Samuel Kaski
RECOMB
2003
Springer
14 years 10 months ago
Phylogenetically and spatially conserved word pairs associated with gene expression changes in yeasts
Background: Transcriptional regulation in eukaryotes often involves multiple transcription factors binding to the same transcription control region, and to understand the regulato...
Derek Y. Chiang, Alan M. Moses, Manolis Kamvysseli...