title: Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification.
abstract: A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective.
authors.affiliation: School of Computer, Central China Normal University, Wuhan 430079, People's Republic of China. jincong@mail.ccnu.ed.cn.
authors.forename: Cong
authors.initials: C
authors.lastname: Jin
authors.affiliation: Département de Physique, École Normale Supérieure, 24, rue Lhomond 75231 Paris Cedex 5, France.
authors.forename: Shu-Wei
authors.initials: SW
authors.lastname: Jin
mesh_terms: D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D009363:Neoplasm Proteins