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» A framework for modelling virus gene expression data
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BMCBI
2008
148views more  BMCBI 2008»
13 years 7 months ago
Discovering biclusters in gene expression data based on high-dimensional linear geometries
Background: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classifi...
Xiangchao Gan, Alan Wee-Chung Liew, Hong Yan
BMCBI
2011
12 years 11 months ago
RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples
Background: A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is dif...
Angel Alvarez, Peter J. Woolf
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 7 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
BMCBI
2006
239views more  BMCBI 2006»
13 years 7 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
CBMS
2005
IEEE
14 years 1 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...