Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Article, Preprint English OPEN
Liu, Yihui ; Aickelin, Uwe ; Feyereisl, Jan ; Durrant, Lindy G (2013)
  • Related identifiers: doi: 10.1016/j.knosys.2012.09.011
  • Subject: Computer Science - Computational Engineering, Finance, and Science | Computer Science - Neural and Evolutionary Computing

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and\ud treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in\ud survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce\ud dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were\ud located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time.
  • References (52)
    52 references, page 1 of 6

    [ 1 ] J.A.D. Simpson, A. Al-Attar, N.F.S. Watson, J.H. Scholefield, M. Ilyas, L.G. Durrant, Intratumoral T cell infiltration, MHC class I and STAT1 as biomarkers of good prognosis in colorectal cancer Gut. 59 (2010) 926-933.

    [2] G.J. Ullenhag, A. Mukherjee, N.F.S.Watson, A. Al-Attar, J.H. Scholefield, and L.G. Durrant, Overexpression of FLIP L is an independent marker of poor prognosis in colorectal cancer patients, Clin Cancer Res. 13 (2007) 5070-5075.

    [3] Y. Liu, Detect key genes information in classification of microarray data, EURASIP Journal on Advances in Signal Processing 612397 (2008).

    [4] Y. Liu, L. Bai, Find significant gene information based on changing points of microarray data, IEEE Transactions on Biomedical Engineering 56 (2009) 1108-1116.

    [5] J. Li, H. Liu, S.K. Ng, L. Wong, Discovery of significant rules for classifying cancer diagnosis data, Bioinformatics 19 (2003) 93-102.

    [6] E.F. Petricoin, A.M. Ardekani, B.A. Hitt, P.J. Levine, V.A. Fusaro, S.M. Steinberg, G.B. Mills, C. Simone, D.A. Fishman, E.C. Kohn, L.A. Liotta, Use of proteomic patterns in serum to identify ovarian cancer, The Lancet 359 (2002) 572-577.

    [ 7 ] C.M. Michener, A.M. Ardekani, E.F. 3rd Petricoin, L.A. Liotta, E.C. Kohn, Genomics and proteomics: application of novel technology to early detection and prevention of cancer, Cancer Detect Prev 26 (2002) 249-255.

    [8] B. Zupan, J. Demsar, M.W. Kattan, J.R. Beck, I. Bratko, Machine learning for survival analysis: a case study on recurrence of prostate cancer, Artif Intell Med. 20 (2000) 59-75.

    [9] F. Ambrogi, N. Lama, P. Boracchi, E. Biganzoli, Selection of artificial neural network models for survival analysis with Genetic Algorithms, Computational Statistics & Data Analysis 52 (2007) 30- 42.

    [10] A. Eleuteri, R. Tagliaferri, L. Milano, S.D. Placido, M.D. Laurentiis, A novel neural networkbased survival analysis model, Neural Networks 16 (2003) 855-864. [ 11 ] I. Stajduhar, B. Dalbelo-Basić, N. Bogunović, Impact of censoring on learning Bayesian networks in survival modeling, Artif Intell Med. 47 (2009) 199-217.

  • Related Research Results (1)
  • Metrics
    No metrics available
Share - Bookmark