title: TC-VGC: a tumor classification system using variations in genes' correlation.
abstract: Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene-gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification.
authors.affiliation: Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea.
authors.forename: Eunji
authors.initials: E
authors.lastname: Shin
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.forename: Jaegyoon
authors.initials: J
authors.lastname: Ahn
authors.forename: Sanghyun
authors.initials: S
authors.lastname: Park
mesh_terms: D000465:Algorithms; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis