Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence

Peterson, Leif E., Ozen, Mustafa, Erdem, Halime, Amini, Andrew, Gomez, Lori, Nelson, Colleen C., & Ittmann, Michael (2005) Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence. In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 14-15 Novermber 2005, San Diego, California.


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-one-out 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.

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7 citations in Scopus
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ID Code: 9326
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
ISBN: 0780393872
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2005 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 04 Sep 2007 00:00
Last Modified: 09 Jun 2010 12:45

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