title: Gene subsets extraction based on Mutual-Information-based Minimum Spanning Trees model.
abstract: In microarray data analysis, filter methods with low time complexity neglect correlation among genes. Metrics to calculate the correlation in some of the methods can not effectively reflect function similarity among genes and time complexity is based on the whole gene set. Therefore, a novel selection model called Mutual-Information-based Minimum Spanning Trees (MIMST) is proposed in this paper, which first uses filter methods to remove non-relevant genes, then computes the interdependence of top-ranked genes, and eliminates the redundant genes. The empirical results show that MIMST can find the smallest significant genes subset with higher classification accuracy compared with other methods.
authors.affiliation: School of Computer Science and Engineering, Southeast University, Nanjing 210096, China. jieyuehe@seu.edu.cn
authors.forename: Jieyue
authors.initials: J
authors.lastname: He
authors.forename: Fang
authors.initials: F
authors.lastname: Zhou
authors.forename: Wei
authors.initials: W
authors.lastname: Zhong
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*