期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 8, 页码 2860-2873出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2829811
关键词
Cancer classification; gene selection; group lasso; heuristic; joint mutual information
类别
资金
- National Natural Science Foundation of China [61572127]
- National Key Research and Development Program of China [2017YFB1400801]
- Key Research and Development Program in Jiangsu Province [BE2015728]
- Collaborative Innovation Center of Wireless Communications Technology
- Spanish Ministry of Economy and Competitiveness through the Project SCHEYARD-Optimization of Scheduling Problems in Container Yards - FEDER funds [DPI2015-65895-R]
Relevant gene selection is crucial for analyzing cancer gene expression datasets including two types of tumors in cancer classification. Intrinsic interactions among selected genes cannot be fully identified by most existing gene selection methods. In this paper, we propose a weighted general group lasso (WGGL) model to select cancer genes in groups. A gene grouping heuristic method is presented based on weighted gene co-expression network analysis. To determine the importance of genes and groups, a method for calculating gene and group weights is presented in terms of joint mutual information. To implement the complex calculation process of WGGL, a gene selection algorithm is developed. Experimental results on both random and three cancer gene expression datasets demonstrate that the proposed model achieves better classification performance than two existing state-of-the-art gene selection methods.
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