Commenced in January 2007
Paper Count: 30184
Diagnosis of Ovarian Cancer with Proteomic Patterns in Serum using Independent Component Analysis and Neural Networks
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079630Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1472
 E. F.Petricoin, "Use of proteomic patterns serum to identify ovarian cancer", The Lancet, vol 359, pp 572-577, 2002.
 K.R.Kozak, "Characterization of serum biomarkers for detection od early stage ovarian cancer,"Proteomics, vol 17,no. 5, pp.4589-4596, September 2005.
 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.
 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.
 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.
 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.
 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.
 S. Y. Yang, "Application of serum seldi proteomic patterns in diagnosis of lung cancer, " BMC cancer, vol. 83, no. September 2005.
 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.
 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.
 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.
 Hyv├ñrinen, A., J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, 2001.
 R. O. Duda, "Pattern Classification and Scene Analysis," Wiley Intercience Publication, New York, 1973.
 C. M. Bishop, "Neural Networks for Pattern Recognition," Oxford University Press, New York, 1999.
 Christoyianni I., "Fast detection of masses in computer-aided mammography,". IEEE Signal Process Mag 2000, no. 7, pp 54-64.
 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.
 K. Fukunaga, "Introduction to Statistical Pattern Recognition," Academic Press, London, 1990.
 Christoyianni I., "Computer aided diagnosis of breast cancer in digitized mammograms,",Comp. Med. Imag. & Graf., no. 26, pp. 309-319, 2002.