Understanding microarray data through applying competent program evolution

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Understanding microarray data through applying competent program evolution

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents

London, England

POSTER SESSION: Biological applications: posters table of contents

Pages: 430 - 430

Year of Publication: 2007

ISBN:978-1-59593-697-4

Authors

Moshe Looks	Washington University in St. Louis: also SAIC, St. Louis, MO
Ben Goertzel	Biomind LLC, Rockville, MD
Lucio de Souza Coelho	Biomind LLC, Rockville, MD
Mauricio Mudado	Biomind LLC, Rockville, MD
Cassio Pennachin	Biomind LLC, Rockville, MD

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	B. Goertzel et al. Learning comprehensible classification rules from gene expression data using genetic programming and biological ontologies. In CIBB, 2006.
	2	M. Looks. Competent Program Evolution. PhD thesis, Washington University in St. Louis, 2006.
	3	T. Lu et al. Gene regulation and DNA damage in the aging human brain. Nature, 2004.
	4	M. A. Shipp et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine, 2002.
	5	Alexander Statnikov , Constantin F. Aliferis , Ioannis Tsamardinos , Douglas Hardin , Shawn Levy, A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis, Bioinformatics, v.21 n.5, p.631-643, March 2005 [doi>10.1093/bioinformatics/bti033]