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:
https://lup.lub.lu.se/record/316357
- author
- Ringnér, Markus
LU
and Peterson, Carsten
LU
- organization
- publishing date
- 2003-03
- 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)
- alternative location
- https://www.future-science.com/doi/pdf/10.2144/mar03ringner
- date added to LUP
- 2016-04-01 15:45:20
- date last changed
- 2024-01-10 19:16:21
@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}},
url = {{https://www.future-science.com/doi/pdf/10.2144/mar03ringner}},
volume = {{34}},
year = {{2003}},
}