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Complexity
Volume 2017, Article ID 8917258, 14 pages
https://doi.org/10.1155/2017/8917258
Research Article

Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

1College of Computer Science, Sichuan University, Chengdu 610065, China
2College of Computer and Information Science, Southwest University, Chongqing 400715, China
3College of Mathematics and Statistics, Southwest University, Chongqing 400715, China
4School of Electronic and Information Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Beilin District, Xi’an 710049, China
5Toxicology Institute, College of Preventive Medicine, Third Military Medical University, 30 Gaotanyan Street, Shapingba District, Chongqing 400038, China
6Department of Environment Health, College of Preventive Medicine, Third Military Medical University, 30 Gaotanyan Street, Shapingba District, Chongqing 400038, China

Correspondence should be addressed to Badong Chen; nc.ude.utjx.liam@dbnehc and Ziyuan Zhou; nc.ude.ummt@uohznauyiz

Received 30 April 2017; Revised 10 July 2017; Accepted 17 August 2017; Published 16 October 2017

Academic Editor: Fang-Xiang Wu

Copyright © 2017 Le Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Colorectal cancer (CRC), as a result of a multistep process and under multiple factors, is one of the most common life-threatening cancers worldwide. To identify the “high risk” populations is critical for early diagnosis and improvement of overall survival rate. Of the complicated genetic and environmental factors, which group is mostly concerning colorectal carcinogenesis remains contentious. For this reason, this study collects relatively complete information of genetic variations and environmental exposure for both CRC patients and cancer-free controls; a multimethod ensemble model for CRC-risk prediction is developed by employing such big data to train and test the model. Our results demonstrate that (1) the explored genetic and environmental biomarkers are validated to connect to the CRC by biological function- or population-based evidences, (2) the model can efficiently predict the risk of CRC after parameter optimization by the big CRC-related data, and (3) our innovated heterogeneous ensemble learning model (HELM) and generalized kernel recursive maximum correntropy (GKRMC) algorithm have high prediction power. Finally, we discuss why the HELM and GKRMC can outperform the classical regression algorithms and related subjects for future study.