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Diagnosis of Ovarian Cancer with Proteomic Patterns in Serum using Independent Component Analysis and Neural Networks

Authors: Simone C. F. Neves, Lúcio F. A. Campos, Ewaldo Santana, Ginalber L. O. Serra, Allan K. Barros

Abstract:

We propose a method for discrimination and classification of ovarian with benign, malignant and normal tissue using independent component analysis and neural networks. The method was tested for a proteomic patters set from A database, and radial basis functions neural networks. The best performance was obtained with probabilistic neural networks, resulting I 99% success rate, with 98% of specificity e 100% of sensitivity.

Keywords: Cancer ovarian, Proteomic patterns in serum, independent component analysis and neural networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079630

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References:


[1] E. F.Petricoin, "Use of proteomic patterns serum to identify ovarian cancer", The Lancet, vol 359, pp 572-577, 2002.
[2] K.R.Kozak, "Characterization of serum biomarkers for detection od early stage ovarian cancer,"Proteomics, vol 17,no. 5, pp.4589-4596, September 2005.
[3] J.K.Yu, "An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer," Jornal of Zhejiang University SCIENCE, vol. 6, no.4, pp. 227-231, July 2005.
[4] G. Ricolleau, " Suface - enhaced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors." Proteomics, vol.6, no.6, pp.1963-1975, November 2006.
[5] D. Donald. "Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles, "Chemometrics and intelligent Laboratory Systems, vol 82, no. 1, pp. 2- 7, January 2006.
[6] W.D. Tong, "Using decision forest to classify prostate cancer sample son the basis of seldi-tof ms data: assessing chance correlation and prediction confidence," Enviromental Health perspective, vol 112, no.16, November 2004.
[7] J. K. Yu, "An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics," World J Gastroenterol, vol.21, no.10, pp. 3127-3131, October 2004.
[8] S. Y. Yang, "Application of serum seldi proteomic patterns in diagnosis of lung cancer, " BMC cancer, vol. 83, no. September 2005.
[9] B. L. Adam, "Serum protein finger printing coupled with a pattern matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and heatthy men ", Cancer Res, vol.62,no.3, pp.609- 614,May 2002.
[10] Y. Qu., "Boosted decision tree analysis of surface - enhaced laser desorption/ionization mass spectral serum profiles discriminated prostate cancer from nonprostate patients," Clin Chem, vol.48, pp.1835-1843, 2002.
[11] G. Ball, "An integrated approach utilizing artificial neural networks and seldi mass spectrometry for the classification of human tumors and rapid dentification of potencial biomarkers," Some Fine Journal, vol.18, no. 3, pp. 395 - 4004, May 2002.
[12] Hyvärinen, A., J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, 2001.
[13] R. O. Duda, "Pattern Classification and Scene Analysis," Wiley Intercience Publication, New York, 1973.
[14] C. M. Bishop, "Neural Networks for Pattern Recognition," Oxford University Press, New York, 1999.
[15] Christoyianni I., "Fast detection of masses in computer-aided mammography,". IEEE Signal Process Mag 2000, no. 7, pp 54-64.
[16] Christoyianni I., "Neural classification of abnormal tissue in digital mammography using statistical features of the texture," IEEE Int Conf Electron, Circuits Systems, no.1, pp. 117-120, 1999.
[17] K. Fukunaga, "Introduction to Statistical Pattern Recognition," Academic Press, London, 1990.
[18] Christoyianni I., "Computer aided diagnosis of breast cancer in digitized mammograms,",Comp. Med. Imag. & Graf., no. 26, pp. 309-319, 2002.