Marker selection for predicting continuous survival period of colorectal cancer

Shibo Liu, Xiaojuan Feng (), Huanyu Zhao, Zhengyou Wang () and Yanan Zhang
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Shibo Liu: Shijiazhuang Tiedao University
Xiaojuan Feng: Shijiazhuang Medical College
Huanyu Zhao: Hebei Academy of Sciences
Zhengyou Wang: Shijiazhuang Tiedao University
Yanan Zhang: Shijiazhuang Tiedao University

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 4, No 5, 785-791

Abstract: 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, $$\chi ^2$$ χ 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.

Keywords: Biomarkers; Colorectal cancer; Feature selection; Particle swarm optimization; BP neural network (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13198-019-00847-0

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