New gene selection method for classification of cancer subtypes considering within-class variation.
In this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well-known problem of gene selection, large within-class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches.
Cho
Ji-Hoon
JH
Department of Chemical Engineering, Pohang University of Science and Technology, San 31 Hyoja-Dong, 790-784 Pohang, South Korea.
Lee
Dongkwon
D
Park
Jin Hyun
JH
Lee
In-Beum
IB
D000208:Acute Disease; D003627:Data Interpretation, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D007938:Leukemia / Q000145:classification / Q000235:genetics / Q000378:metabolism; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*