Abstract
Colorectal cancer is one of the most prevalent cancers that usually has a strong concealment. For early detection and prevention of colorectal cancer, various type of biomarkers are checked to verify whether they can accurately and sensitively assess this disease. Though there have existed some traditional statistical methodologies for this verification, such as t-test, \(\chi ^2\)-test and information gain, it is hard to apply the univariate technology for mining massive biomarker set. In this paper, we proposes a hybrid algorithm (BPPSO) based on particle swarm optimization combining with back-propagation neural network to select critical biomarkers for predicting continuous survival period. The experiments show that BPPSO is effective for biomarker selection problem.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Al-Bahrani R, Agrawal A, Choudhary A (2013) Colon cancer survival prediction using ensemble data mining on seer data. In: 2013 IEEE international conference on big data, pp 9–16
Alexander S, Anton B et al (2013) Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model. Ann Surg Oncol 20(1):161–174
Bush DM, Michaelson JS (2009) Derivation: nodes + prognostic factors equation for colon cancer accuracy of the nodes + prognostic factors equation, technical report
Chang GJ, Hu CY, Eng C, Skibber JM, Rodriguez-Bigas M (2009) Practical application of a calculator for conditional survival in colon cancer. J Clin Oncol 27(35):5938–5943
Dolgobrodov SG, Moore P, Marshall R, Bittern R, Steele RJ, Cuschieri A (2007) Artificial neural network: predicted versus observed survival in patients with colonic cancer. Dis Colon Rectum 50(2):184–91
Farid EA (2005) Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer 4:29
Jansson A, Gentile M, Sun X (2010) p53 Mutations are present in colorectal cancer with cytoplasmic p53 accumulation. Int J Cancer J Int Du Cancer 92(3):338
Kim K-J, Cho S-B (2014) Prediction of colon cancer using an evolutionary neural network. Neurocomputing 61:361–379
Liu X, Chen Z (2012) Research of data mining on the post-treatment survival period prediction of colorectal cancer. In: Proceedings of the international conference on information engineering and applications (IEA), pp 265–270
Liu Z, Chen D, Tian G, Tang ML, Tan M, Sheng L (2010) Efficient support vector machine method for survival prediction with SEER data. Adv Comput Biol 680:11–18
Liu Y, Aickelin U, Feyereisl J, Lindy GD (2013) Biomarker CD46 detection in colorectal cancer data based on wavelet feature extraction and genetic algorithm. Knowl Based Syst 37:502–514
Mohamad P, Sedigheh K, Mohammad Z (2017) Comparison of basic and ensemble data mining methods in predicting 5-year survival of colorectal cancer patients. Acta Inform Med 25(4):254–258
Peng JH, Fang YJ, Li CX et al (2016) A scoring system based on artificial neural network for predicting 10-year survival in stage II. A colon cancer patients after radical surgery. Oncotarget 7(16):22939–22947
Popovic D, Sifrim A, Pavlopoulos GA et al (2012) A simple genetic algorithm for biomarker mining. In: Shibuya T, Kashima H, Sese J, Ahmad S (eds) Pattern recognition in bioinformatics. PRIB 2012. Lecture notes in computer science, vol 7632. Springer, Berlin, Heidelberg, pp 222–232. https://doi.org/10.1007/978-3-642-34123-6_20
Saeys Y, Inza I et al (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517
Sailer F, Pobiruchin M, Bochum S, Martens UM, Schramm W (2015) Prediction of 5-year survival with data mining algorithms. In: ICIMTH, pp 75–8
Silva A, Oliveira T, Neves J, Novais P (2016) Treating colon cancer survivability prediction as a classification problem. Adv Distrib Comput Artif Intell J 5(1):37–50
Snow PB, Kerr DJ, Brandt JM, Rodvold DM (2001) Neural network and regression predictions of 5-year survival after colon carcinoma treatment. Cancer 91(8):1673–1678
Spelt L, Nilsson J, Andersson R, Andersson B (2013) Artificial neural networks—a method for prediction of survival following liver resection for colorectal cancer metastases. Eur J Surg Oncol 39(6):648–54
Stojadinovic A, Nissan A, Eberhardt J, Chua TC, Pelz JO, Esquivel J (2011) Development of a bayesian belief network model for personalized prognostic risk assessment in colon carcinomatosis. Am Surg 77(2):221–30
Sun XF (2002) p73 Overexpression is a prognostic factor in patients with colorectal adenocarcinoma. Clin Cancer Res 8(1):165–170
Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252
Weiser MR, Gonen M et al (2011) Predicting survival after curative colectomy for cancer: individualizing colon cancer staging. J Clin Oncol 29(36):4796–4802
Zhang XY, Hu XM, Lin Y (2014) Performance comparison analysis of genetic algorithm and particle swarm optimization algorithm. J Front Comput Sci Technol 8(1):90–102
Acknowledgements
This research is supported by Funding Project from Health Commission of Hebei Province (NO. 20191505), the Key Science and Technology Project of Hebei Provincial Education Department (NO. ZD2017247), the Shijiazhuang Tiedao University Graduate Practice Base Funding Project (NO. Z671180101) and the Science and Technology Project of Hebei Academy of Sciences (NO. 19607).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, S., Feng, X., Zhao, H. et al. Marker selection for predicting continuous survival period of colorectal cancer. Int J Syst Assur Eng Manag 11, 785–791 (2020). https://doi.org/10.1007/s13198-019-00847-0
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-019-00847-0