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GECCO
2007
Springer
179views Optimization» more  GECCO 2007»
13 years 11 months ago
Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms
Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomark...
Alper Küçükural, Reyyan Yeniterzi...
BMCBI
2004
205views more  BMCBI 2004»
13 years 4 months ago
A combinational feature selection and ensemble neural network method for classification of gene expression data
Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
BMCBI
2005
190views more  BMCBI 2005»
13 years 4 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
BMCBI
2008
85views more  BMCBI 2008»
13 years 5 months ago
Merging microarray data from separate breast cancer studies provides a robust prognostic test
Background: There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have...
Lei Xu, Aik Choon Tan, Raimond L. Winslow, Donald ...
IEAAIE
2010
Springer
13 years 2 months ago
Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles
Abstract. Gene expression profiling strategies have attracted considerable interest from biologist due to the potential for high throughput analysis of hundreds of thousands of gen...
Daniel Urda, José Luis Subirats, Leonardo F...