DocumentCode
472222
Title
Cancer Classification Using Loss of Heterozygosity Data Derived from Single-Nucleotide Polymorphism Genotyping Arrays
Author
Wang, Yuhang
Author_Institution
Dept. of Comput. Sci. & Eng., Southern Methodist Univ., Dallas, TX
fYear
2006
fDate
Aug. 30 2006-Sept. 3 2006
Firstpage
5864
Lastpage
5867
Abstract
Single-Nucleotide Polymorphism (SNP) array is a recently introduced technology that genotypes more than 10,000 human SNPs on a single array. It has been shown that genome-wide Loss of Heterozygosity (LOH) calls can be derived by analyzing the genotypes calls measured by SNP arrays using paired tumor and normal tissue samples. The goal of this study is to evaluate the possibility of cancer classification using LOH calls. As a proof of concept, we applied 16 different combinations of classification algorithms and feature selection algorithms to a public data set that contains LOH calls of 10,043 SNP loci obtained from 10 breast cancer patients and 5 small cell lung cancer (SCLC) patients. Performance was measured in terms of the leave-one-out cross-validation (LOOCV) classification accuracy. Experimental results suggest that LOH calls derived from SNP arrays can be an excellent indicator of cancer type
Keywords
cancer; cellular biophysics; genetics; gynaecology; learning (artificial intelligence); medical computing; molecular biophysics; pattern classification; polymorphism; support vector machines; tumours; breast cancer classification; feature selection algorithm; genome; genotype analysis; human single-nucleotide polymorphism genotyping arrays; leave-one-out cross-validation classification accuracy; loss-of-heterozygosity data; machine learning; normal tissue samples; paired tumor; public data set; small cell lung cancer patient; support vector machine; Bioinformatics; Cancer; Classification algorithms; Gene expression; Genomics; Humans; Loss measurement; Machine learning; Machine learning algorithms; Neoplasms;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location
New York, NY
ISSN
1557-170X
Print_ISBN
1-4244-0032-5
Electronic_ISBN
1557-170X
Type
conf
DOI
10.1109/IEMBS.2006.260116
Filename
4463141
Link To Document