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» A Theoretical Analysis of Gene Selection
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KDD
2001
ACM
169views Data Mining» more  KDD 2001»
14 years 9 months ago
Hierarchical cluster analysis of SAGE data for cancer profiling
In this paper we present a method for clustering SAGE (Serial Analysis of Gene Expression) data to detect similarities and dissimilarities between different types of cancer on the...
Jörg Sander, Monica C. Sleumer, Raymond T. Ng
APBC
2003
128views Bioinformatics» more  APBC 2003»
13 years 10 months ago
Machine Learning in DNA Microarray Analysis for Cancer Classification
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it e...
Sung-Bae Cho, Hong-Hee Won
JCP
2006
157views more  JCP 2006»
13 years 9 months ago
CF-GeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference
Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper present...
Muhammad Shoaib B. Sehgal, Iqbal Gondal, Laurence ...
BMCBI
2008
160views more  BMCBI 2008»
13 years 9 months ago
Feature selection environment for genomic applications
Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e....
Fabrício Martins Lopes, David Correa Martin...
WABI
2010
Springer
167views Bioinformatics» more  WABI 2010»
13 years 7 months ago
Quantifying the Strength of Natural Selection of a Motif Sequence
Quantification of selective pressures on regulatory sequences is a central question in studying the evolution of gene regulatory networks. Previous methods focus primarily on sing...
Chen-Hsiang Yeang