title: Classifying subtypes of acute lymphoblastic leukemia using silhouette statistics and genetic algorithms.
abstract: Correct classification and prediction of tumor cells is essential for a successful diagnosis and reliable future treatment. In this study, we aimed at using genetic algorithms for feature selection and proposed silhouette statistics as a discriminant function to distinguish between six subtypes of pediatric acute lymphoblastic leukemia by using microarray with thousands of gene expressions. Our methods have shown a better classification accuracy than previously published methods and obtained a set of genes effective to discriminate subtypes of pediatric acute lymphoblastic leukemia. Furthermore, the use of silhouette statistics, offering the advantages of measuring the classification quality by a graphical display and by an average silhouette width, has also demonstrated feasibility and novelty for more difficult multiclass tumor prediction problems.
authors.affiliation: Department of Computer Science and Engineering, Dahan Institute of Technology, Hualien, 970, Taiwan, ROC. lintsunc@ms01.dahan.edu.tw
authors.forename: Tsun-Chen
authors.initials: TC
authors.lastname: Lin
authors.forename: Ru-Sheng
authors.initials: RS
authors.lastname: Liu
authors.forename: Ya-Ting
authors.initials: YT
authors.lastname: Chao
authors.forename: Shu-Yuan
authors.initials: SY
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D002648:Child; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics*