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WCE
2007
13 years 6 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
BMCBI
2007
173views more  BMCBI 2007»
13 years 5 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...
SAC
2008
ACM
13 years 3 months ago
Strangeness-based feature weighting and classification of gene expression profiles
Achieving high classification accuracy is a major challenge in the diagnosis of cancer types based on gene expression profiles. These profiles are notoriously noisy in that a larg...
Haifeng Shao, Bei Yu, Joseph H. Nadeau
FUIN
2002
123views more  FUIN 2002»
13 years 5 months ago
Learning Rough Set Classifiers from Gene Expressions and Clinical Data
Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capabilit...
Herman Midelfart, Henryk Jan Komorowski, Kristin N...
IEEEMM
2007
146views more  IEEEMM 2007»
13 years 5 months ago
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N...