Tissue Classification with Gene Expression Profiles

DMCA

Tissue Classification with Gene Expression Profiles (2000)

Cached

Download Links

[www.cs.huji.ac.il]
[cbio.ensmp.fr]
[www.cs.huji.ac.il]

Other Repositories/Bibliography

DBLP

by Amir Ben-Dor , Laurakay Bruhn , Agilent Laboratories , Nir Friedman , Miche`l Schummer , Iftach Nachman , U. Washington , U. Washington , Zohar Yakhini

Venue:	Journal of Computational Biology
Citations:	239 - 11 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}
}

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 The second consists of 100,000 clones, measured in 32 ovarian samples (unpublished, extension of data set described in [26]).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...

Keyphrases

tissue classification gene expression profile gene expression data cellular process epithelial colon sample individual gene expression level leave-one-out cross validation gene expression normal clinical sample complete expression profile high dimensional classification method classification power data set ovarian sample classification platform second consists efficient cancer diagnosis present result

DMCA