title: Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis.
abstract: Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China. sunxin1984@yahoo.com.cn
authors.forename: Xin
authors.initials: X
authors.lastname: Sun
authors.forename: Yanheng
authors.initials: Y
authors.lastname: Liu
authors.forename: Da
authors.initials: D
authors.lastname: Wei
authors.forename: Mantao
authors.initials: M
authors.lastname: Xu
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
authors.forename: Jiawei
authors.initials: J
authors.lastname: Han
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D007257:Information Theory; D008297:Male; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D005819:Genetic Markers