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Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

Author

Listed:
  • Le Zhang
  • Chunqiu Zheng
  • Tian Li
  • Lei Xing
  • Han Zeng
  • Tingting Li
  • Huan Yang
  • Jia Cao
  • Badong Chen
  • Ziyuan Zhou

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.

Suggested Citation

  • Le Zhang & Chunqiu Zheng & Tian Li & Lei Xing & Han Zeng & Tingting Li & Huan Yang & Jia Cao & Badong Chen & Ziyuan Zhou, 2017. "Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer," Complexity, Hindawi, vol. 2017, pages 1-14, October.
    • Handle: RePEc:hin:complx:8917258
    DOI: 10.1155/2017/8917258
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    References listed on IDEAS

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    1. Chris A. Mattmann, 2013. "A vision for data science," Nature, Nature, vol. 493(7433), pages 473-475, January.
    2. Jiang, Beini & Dai, Weizhong & Khaliq, Abdul & Carey, Michelle & Zhou, Xiaobo & Zhang, Le, 2015. "Novel 3D GPU based numerical parallel diffusion algorithms in cylindrical coordinates for health care simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 109(C), pages 1-19.
    3. Hailiang Huang & Pritam Chanda & Alvaro Alonso & Joel S Bader & Dan E Arking, 2011. "Gene-Based Tests of Association," PLOS Genetics, Public Library of Science, vol. 7(7), pages 1-15, July.
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    Cited by:

    1. Le Zhang & Wanyu Bai & Na Yuan & Zhenglin Du, 2019. "Comprehensively benchmarking applications for detecting copy number variation," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-12, May.

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