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

doi:10.1109/cibcb.2005.1594929

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 Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 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 language	English (US)
Title of host publication	Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	0780393872, 9780780393875
DOIs	https://doi.org/10.1109/cibcb.2005.1594929
State	Published - 2005
Event	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 - La Jolla, CA, United States Duration: Nov 14 2005 → Nov 15 2005

Publication series

Name	Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Volume	2005

Other

Other	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Country/Territory	United States
City	La Jolla, CA
Period	11/14/05 → 11/15/05

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/cibcb.2005.1594929

Cite this

Peterson, L. E., Ozen, M., Erdem, H., Amini, A., Gomez, L., Nelson, C. C., & Ittmann, M. (2005). Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 [1594929] (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05; Vol. 2005). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cibcb.2005.1594929

Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. / Peterson, Leif E.; Ozen, Mustafa; Erdem, Halime et al.

Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. Institute of Electrical and Electronics Engineers Inc., 2005. 1594929 (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Peterson, LE, Ozen, M, Erdem, H, Amini, A, Gomez, L, Nelson, CC & Ittmann, M 2005, Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. in Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05., 1594929, Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05, vol. 2005, Institute of Electrical and Electronics Engineers Inc., 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05, La Jolla, CA, United States, 11/14/05. https://doi.org/10.1109/cibcb.2005.1594929

Peterson LE, Ozen M, Erdem H, Amini A, Gomez L, Nelson CC et al. Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. Institute of Electrical and Electronics Engineers Inc. 2005. 1594929. (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05). https://doi.org/10.1109/cibcb.2005.1594929

Peterson, Leif E. ; Ozen, Mustafa ; Erdem, Halime et al. / Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. Institute of Electrical and Electronics Engineers Inc., 2005. (Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05).

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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.",

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AB - 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.

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

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