title: A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy.
abstract: OBJECTIVE
  Gene selection method as an important data preprocessing work has been followed. The criteria Maximum relevance and minimum redundancy (MRMR) has been commonly used for gene selection, which has a satisfactory performance in evaluating the correlation between two genes. However, for viewing genes in isolation, it ignores the influence of other genes.
  
  
  METHODS
  In this study, we propose a new method based on sparse representation and MRMR algorithm (SRCMRM), using the sparse representation coefficient to represent the relevance of genes and correlation between genes and categories. The SRCMRMR algorithm contains two steps. Firstly, the genes irrelevant to the classification target are removed by using sparse representation coefficient. Secondly, sparse representation coefficient is used to calculate the correlation between genes and the most representative gene with the highest evaluation.
  
  
  RESULTS
  To validate the performance of the SRCMRM, our method is compared with various algorithms. The proposed method achieves better classification accuracy for all datasets.
  
  
  CONCLUSIONS
  The effectiveness and stability of our method have been proven through various experiments, which means that our method has practical significance.
authors.affiliation: School of Computer Science and Technology, Hunan Institute of Technology, 421002 Hengyang, China.
authors.forename: Min
authors.initials: M
authors.lastname: Chen
authors.affiliation: School of Information Science and Engineering, Hunan University, Changsha 410082, China.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: He
authors.affiliation: Department of Physical Education, Hunan Institute of Technology, 421002 Hengyang, China.
authors.forename: ShaoBin
authors.initials: S
authors.lastname: Duan
authors.affiliation: College of Computer Science and Information Technology, Central South University of Forestry and Technology, 410081 Changsha, Hunan, China.
authors.forename: YingWei
authors.initials: Y
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling; D005821:Genetic Techniques; D006801:Humans; D046228:Microarray Analysis / Q000379:methods; D060388:Support Vector Machine
keywords: MRMR; Sparse representation; gene selection