title: McTwo: a two-step feature selection algorithm based on maximal information coefficient.
abstract: BACKGROUND
  High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.
  
  
  RESULTS
  This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.
  
  
  CONCLUSIONS
  McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P.R. China.
authors.forename: Ruiquan
authors.initials: R
authors.lastname: Ge
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P.R. China.
authors.forename: Manli
authors.initials: M
authors.lastname: Zhou
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|School of Science, Hubei University of Technology, Wuhan, Hubei, 430068, P.R. China.
authors.forename: Youxi
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P.R. China.
authors.forename: Qinghan
authors.initials: Q
authors.lastname: Meng
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.
authors.forename: Guoqin
authors.initials: G
authors.lastname: Mai
authors.affiliation: Shenzhen Children's Hospital, Shenzhen, Guangdong, 518026, P.R. China. madl1234@126.com.
authors.forename: Dongli
authors.initials: D
authors.lastname: Ma
authors.affiliation: Department of Pathogenobiology, Basic Medical College of Jilin University, Changchun, Jilin, China. qing@jlu.edu.cn.
authors.forename: Guoqing
authors.initials: G
authors.lastname: Wang
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China. FengfengZhou@gmail.com.
authors.forename: Fengfeng
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D012984:Software
keywords: Feature selection; Filter algorithm; Heuristic algorithm; Maximal information coefficient (MIC); Wrapper algorithm