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BIBE
2007
IEEE
136views Bioinformatics» more  BIBE 2007»
15 years 1 months ago
A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
Yi Zhang, Chris H. Q. Ding, Tao Li
111
Voted
SAC
2004
ACM
15 years 5 months ago
Time-frequency feature detection for time-course microarray data
Gene clustering based on microarray data provides useful functional information to the working biologists. Many current gene-clustering algorithms rely on Euclidean-based distance...
Jiawu Feng, Paolo Emilio Barbano, Bud Mishra
WCE
2007
15 years 24 days 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
2006
155views more  BMCBI 2006»
14 years 11 months ago
Analysis of promoter regions of co-expressed genes identified by microarray analysis
Background: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biol...
Srinivas Veerla, Mattias Höglund
CBMS
2006
IEEE
15 years 1 months ago
Incorporating Gene Ontology in Clustering Gene Expression Data
In this paper we consider a general framework for clustering expression data that permits integration of various biological data sources through combination of corresponding dissi...
Rafal Kustra, Adam Zagdanski