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Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Khan, J; Wei, JS; Ringnér, Markus LU ; Saal, Lao LU ; Ladanyi, M; Westermann, F; Berthold, F; Schwab, M; Antonescu, CR and Peterson, Carsten LU , et al. (2001) In Nature Medicine 7(6). p.673-679
Abstract
The purpose of this study was to develop a method of classifying cancers to specific diagnosticcategories based on their gene expression signatures using artificial neural networks (ANNs).We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancersbelong to four distinct diagnostic categories and often present diagnostic dilemmas in clinicalpractice. The ANNs correctly classified all samples and identified the genes most relevant to theclassification. Expression of several of these genes has been reported in SRBCTs, but most havenot been associated with these cancers. To test the ability of the trained ANN models to recognizeSRBCTs, we analyzed additional blinded samples that were not previously used for... (More)
The purpose of this study was to develop a method of classifying cancers to specific diagnosticcategories based on their gene expression signatures using artificial neural networks (ANNs).We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancersbelong to four distinct diagnostic categories and often present diagnostic dilemmas in clinicalpractice. The ANNs correctly classified all samples and identified the genes most relevant to theclassification. Expression of several of these genes has been reported in SRBCTs, but most havenot been associated with these cancers. To test the ability of the trained ANN models to recognizeSRBCTs, we analyzed additional blinded samples that were not previously used for the trainingprocedure, and correctly classified them in all cases. This study demonstrates the potentialapplications of these methods for tumor diagnosis and the identification of candidate targets fortherapy. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Medicine
volume
7
issue
6
pages
673 - 679
publisher
Nature Publishing Group
external identifiers
  • wos:000169081500034
  • scopus:0034954414
ISSN
1546-170X
DOI
10.1038/89044
language
English
LU publication?
yes
id
b218d818-9f56-47ca-aaae-1ae3bb1766c1 (old id 131195)
date added to LUP
2007-07-20 12:32:33
date last changed
2018-08-19 04:09:33
@article{b218d818-9f56-47ca-aaae-1ae3bb1766c1,
  abstract     = {The purpose of this study was to develop a method of classifying cancers to specific diagnosticcategories based on their gene expression signatures using artificial neural networks (ANNs).We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancersbelong to four distinct diagnostic categories and often present diagnostic dilemmas in clinicalpractice. The ANNs correctly classified all samples and identified the genes most relevant to theclassification. Expression of several of these genes has been reported in SRBCTs, but most havenot been associated with these cancers. To test the ability of the trained ANN models to recognizeSRBCTs, we analyzed additional blinded samples that were not previously used for the trainingprocedure, and correctly classified them in all cases. This study demonstrates the potentialapplications of these methods for tumor diagnosis and the identification of candidate targets fortherapy.},
  author       = {Khan, J and Wei, JS and Ringnér, Markus and Saal, Lao and Ladanyi, M and Westermann, F and Berthold, F and Schwab, M and Antonescu, CR and Peterson, Carsten and Meltzer, PS},
  issn         = {1546-170X},
  language     = {eng},
  number       = {6},
  pages        = {673--679},
  publisher    = {Nature Publishing Group},
  series       = {Nature Medicine},
  title        = {Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks},
  url          = {http://dx.doi.org/10.1038/89044},
  volume       = {7},
  year         = {2001},
}