Artificial neural network analysis of DNA microarray-based prostate cancer recurrence

Leif E. Peterson, Mustafa Ozen, Halime Erdem, Andrew Amini, Lori Gomez, Colleen C. Nelson, Michael Ittmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

DNA microarray-based gene expression profiles have been established for a variety of adult cancers. This paper addresses application of an artificial neural network (ANN) with leave-oneout testsing and 8-fold cross-validation for analyzing DNA microarray data to identify genes predictive of recurrence after prostatectomy. Among 725 genes screened for ANN input, a 16-gene model resulted in 99-100% diagnostic sensitivity and specificity: DGCR5, FLJ10618, RIS1, PRO1855, ABCB9, AK057203, GOLGA5, HARS, AK024152, HEP27, PPIA, SNRPF, SULT1A3, SECTM1, EIF4EBP1, and S71435. Genes identified with ANN that are prognostic of prostate cancer recurrence may be either causal for prostate cancer or secondary to the disease. Nevertheless, the genes identified may be confirmed in the future to be markers of early detection and/or therapy.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)0780393872, 9780780393875
DOIs
StatePublished - 2005
Event2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 - La Jolla, CA, United States
Duration: Nov 14 2005Nov 15 2005

Publication series

NameProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Volume2005

Other

Other2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
CountryUnited States
CityLa Jolla, CA
Period11/14/0511/15/05

ASJC Scopus subject areas

  • Engineering(all)

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