Title :
Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence
Author :
Peterson, L.E. ; Ozen, M. ; Erdem, H. ; Amini, A. ; Gomez, L. ; Nelson, C.C. ; Ittmann, M.
Author_Institution :
Dept. of Medicine and Dept. of Molecular and Human Genetics Baylor College of Medicine, Houston, Texas 77030, USA.; 1709 Dryden Street, Suite 1025, Houston, Texas 77030, USA. Telephone: +001
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-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.
Keywords :
Artificial neural networks; Clinical diagnosis; DNA; Data analysis; Diseases; Gene expression; Neoplasms; Pathology; Prostate cancer; Testing;
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. Proceedings of the 2005 IEEE Symposium on
Conference_Location :
La Jolla, CA, USA
Print_ISBN :
0-7803-9387-2
DOI :
10.1109/CIBCB.2005.1594929
