Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

• Published in: Complexity (New York, N.Y.) Vol. 2017; no. 2017; pp. 1 - 14
• Main Authors: Zhou, Ziyuan, Chen, Badong, Yang, Huan, Li, Tingting, Zeng, Han, Xing, Lei, Li, Tian, Zheng, Chunqiu, Zhang, Le, Cao, Jia
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Alcohol; Algorithms; Bioinformatics; Biology; Biomarkers; Biomedical engineering; Cancer; Carcinogens; Colorectal cancer; Data management; Deoxyribonucleic acid; Diagnosis; DNA; Epigenetics; Food; Game theory; Genes; Genomics; Machine learning; Mathematical models; Medical prognosis; Metabolism; Model testing; Risk assessment; Risk factors; Robustness (mathematics); Studies; Survival analysis; China
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2017/8917258

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.