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» Class discovery in gene expression data
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BMCBI
2007
173views more  BMCBI 2007»
13 years 7 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
PSB
2004
13 years 9 months ago
Motif Discovery in Heterogeneous Sequence Data
This paper introduces the first integrated algorithm designed to discover novel motifs in heterogeneous sequence data, which is comprised of coregulated genes from a single genome...
Amol Prakash, Mathieu Blanchette, Saurabh Sinha, M...
BIBE
2001
IEEE
188views Bioinformatics» more  BIBE 2001»
13 years 11 months ago
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. How...
Chun Tang, Li Zhang, Aidong Zhang, Murali Ramanath...
SP
2008
IEEE
134views Security Privacy» more  SP 2008»
13 years 7 months ago
Discover gene specific local co-regulations from time-course gene expression data
Discovering gene co-regulatory relationships is one of most important research in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a p...
Ji Zhang, Qigang Gao, Hai H. Wang
CIBCB
2006
IEEE
14 years 1 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...