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Integrating gene ontology into discriminative powers of genes for feature selection in microarray data
One of the main challenges in the classification of microarray gene expression data is the small sample size compared with the large number of genes, so feature selection is an essential step toExpand
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Expression Quantitative Trait Loci Acting Across Multiple Tissues Are Enriched in Inherited Risk for Coronary Artery Disease
Background—Despite recent discoveries of new genetic risk factors, the majority of risk for coronary artery disease (CAD) remains elusive. As the most proximal sensor of DNA variation, RNA abundanceExpand
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kruX: matrix-based non-parametric eQTL discovery
BackgroundThe Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness againstExpand
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Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests
MOTIVATION Transcriptional regulatory network inference methods have been studied for years. Most of them rely on complex mathematical and algorithmic concepts, making them hard to adapt,Expand
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A regression tree-based Gibbs sampler to learn the regulation programs in a transcription regulatory module network
Many algorithms have been proposed to learn transcription regulatory networks from gene expression data. Bayesian networks have obtained promising results, in particular, the module network method.Expand
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Gene Ontology Driven Feature Selection from Microarray Gene Expression Data
  • Jianlong Qi, J. Tang
  • Computer Science
  • IEEE Symposium on Computational Intelligence and…
  • 1 September 2006
One of the main challenges in the classification of microarray gene expression data is the small sample size compared with the large number of genes, so feature selection is an essential step toExpand
  • 9
An integrative approach to infer regulation programs in a transcription regulatory module network
The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents aExpand
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Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network
The module network method has been widely used to infer transcriptional regulatory network from gene expression data. A common strategy of module network learning algorithms is to apply regressionExpand
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Inferring regulation programs in a transcription regulatory module network
Cells have a complex mechanism to control the expression of genes so that they are capable of adapting to environmental changes or genetic perturbations. A major part of the mechanism is fulfilled byExpand