Advanced

Microarray-based cancer diagnosis with artificial neural networks

Ringnér, Markus LU and Peterson, Carsten LU (2003) In BioTechniques 34(Suppl). p.30-30
Abstract
In recent years, the advent of experimental methods top robe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. This article surveys the application of machine learning algorithms to classification and diagnosis of cancer based on expression profiles. To exemplify the important issues of the classification procedure, the emphasis of this article is on one such method, namely artificial neural networks. In addition, methods to extract genes that are... (More)
In recent years, the advent of experimental methods top robe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. This article surveys the application of machine learning algorithms to classification and diagnosis of cancer based on expression profiles. To exemplify the important issues of the classification procedure, the emphasis of this article is on one such method, namely artificial neural networks. In addition, methods to extract genes that are important for the performance of a classifier, as well as the influence of sample selection on prediction results are discussed. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BioTechniques
volume
34
issue
Suppl
pages
30 - 30
publisher
Informa Healthcare
external identifiers
  • wos:000181595900005
  • scopus:0037338017
ISSN
0736-6205
language
English
LU publication?
yes
id
77bbd012-bb73-45ad-8523-6f6daf30b03a (old id 316357)
date added to LUP
2007-09-20 17:36:41
date last changed
2017-08-20 04:18:17
@article{77bbd012-bb73-45ad-8523-6f6daf30b03a,
  abstract     = {In recent years, the advent of experimental methods top robe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. This article surveys the application of machine learning algorithms to classification and diagnosis of cancer based on expression profiles. To exemplify the important issues of the classification procedure, the emphasis of this article is on one such method, namely artificial neural networks. In addition, methods to extract genes that are important for the performance of a classifier, as well as the influence of sample selection on prediction results are discussed.},
  author       = {Ringnér, Markus and Peterson, Carsten},
  issn         = {0736-6205},
  language     = {eng},
  number       = {Suppl},
  pages        = {30--30},
  publisher    = {Informa Healthcare},
  series       = {BioTechniques},
  title        = {Microarray-based cancer diagnosis with artificial neural networks},
  volume       = {34},
  year         = {2003},
}