A deep learning-based multi-model ensemble method for cancer prediction.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 14, 2017

Abstract

BACKGROUND AND OBJECTIVE: Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others.

Authors

  • Yawen Xiao
    Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai 200240, China. Electronic address: foreverxyw@sjtu.edu.cn.
  • Jun Wu
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Zongli Lin
    Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, P.O. Box 400743, Charlottesville, VA 22904-4743, USA. Electronic address: zl5y@virginia.edu.
  • Xiaodong Zhao
    Shandong Vocational Animal Science and Veterinary College, Weifang, China.

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