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Analyzing tumor gene expression profiles

Peterson, Carsten LU and Ringnér, Markus LU (2003) In Artificial Intelligence in Medicine 28(1). p.59-74
Abstract
A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes according to their importance for the classification are explored. The approaches are illustrated by exploratory studies using two examples of retrospective clinical data from routine tests; diagnostic prediction of small round blue cell tumors (SRBCT) of childhood and determining the estrogen receptor (ER) status of sporadic breast cancer. The... (More)
A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes according to their importance for the classification are explored. The approaches are illustrated by exploratory studies using two examples of retrospective clinical data from routine tests; diagnostic prediction of small round blue cell tumors (SRBCT) of childhood and determining the estrogen receptor (ER) status of sporadic breast cancer. The classification performance is gauged using blind tests. These studies demonstrate the feasibility of machine learning-based molecular cancer classification. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
biomformatics, artificial neural networks, diagnostic prediction, target identification, drug, microarray, genes
in
Artificial Intelligence in Medicine
volume
28
issue
1
pages
59 - 74
publisher
Elsevier
external identifiers
  • pmid:12850313
  • wos:000184164800003
  • scopus:0037486916
ISSN
1873-2860
DOI
language
English
LU publication?
yes
id
cd5a78f3-37c4-4669-a04f-eed91b322980 (old id 306165)
date added to LUP
2007-08-03 14:53:55
date last changed
2018-05-29 10:37:33
@article{cd5a78f3-37c4-4669-a04f-eed91b322980,
  abstract     = {A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes according to their importance for the classification are explored. The approaches are illustrated by exploratory studies using two examples of retrospective clinical data from routine tests; diagnostic prediction of small round blue cell tumors (SRBCT) of childhood and determining the estrogen receptor (ER) status of sporadic breast cancer. The classification performance is gauged using blind tests. These studies demonstrate the feasibility of machine learning-based molecular cancer classification.},
  author       = {Peterson, Carsten and Ringnér, Markus},
  issn         = {1873-2860},
  keyword      = {biomformatics,artificial neural networks,diagnostic prediction,target identification,drug,microarray,genes},
  language     = {eng},
  number       = {1},
  pages        = {59--74},
  publisher    = {Elsevier},
  series       = {Artificial Intelligence in Medicine},
  title        = {Analyzing tumor gene expression profiles},
  url          = {http://dx.doi.org/},
  volume       = {28},
  year         = {2003},
}