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BMCBI
2007
157views more  BMCBI 2007»
13 years 7 months ago
Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many i...
Feng Luo, Yunfeng Yang, Jianxin Zhong, Haichun Gao...
CIBCB
2007
IEEE
13 years 11 months ago
Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression
Abstract-- The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ...
Ji He, Xinbin Dai, Xuechun Zhao
BMCBI
2010
153views more  BMCBI 2010»
13 years 7 months ago
GOAL: A software tool for assessing biological significance of genes groups
Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then us...
Alain B. Tchagang, Alexander Gawronski, Hugo B&eac...
BIODATAMINING
2008
135views more  BIODATAMINING 2008»
13 years 7 months ago
Fast Gene Ontology based clustering for microarray experiments
Background: Analysis of a microarray experiment often results in a list of hundreds of diseaseassociated genes. In order to suggest common biological processes and functions for t...
Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi
BMCBI
2006
123views more  BMCBI 2006»
13 years 7 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger