Tissue Classification with Gene Expression Profiles (2000)
| Venue: | Journal of Computational Biology |
| Citations: | 143 - 9 self |
BibTeX
@ARTICLE{Ben-Dor00tissueclassification,
author = {Amir Ben-Dor and Laurakay Bruhn and Agilent Laboratories and Nir Friedman and Miche`l Schummer and Iftach Nachman and U. Washington and U. Washington and Zohar Yakhini},
title = {Tissue Classification with Gene Expression Profiles},
journal = {Journal of Computational Biology},
year = {2000},
volume = {7},
pages = {559--584}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine two sets of gene expression data measured across sets of tumor and normal clinical samples. One set consists of 2,000 genes, measured in 62 epithelial colon samples [1]. The second consists of 100,000 clones, measured in 32 ovarian samples (unpublished, extension of data set described in [26]). We examine the use of scoring methods, measuring separation of tumors from normals using individual gene expression levels. These are then coupled with high dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the two data sets, employing SVM [8], AdaB...
