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Analyzing array data using supervised methods

Ringnér, Markus LU ; Peterson, Carsten LU and Khan, J (2002) In Pharmacogenomics 3(3). p.403-415
Abstract
Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods... (More)
Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods will influence the results and their interpretation, therefore it is important to be familiar with each method, its scope and limitations. Here, methods with special reference to applications for pharmacogenomics are reviewed. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
machine learning, genes, drug targets, DNA chip, diagnostic prediction, diagnostic classification, artificial neural networks, bioinformatics, microarray, support vector machines, target identification
in
Pharmacogenomics
volume
3
issue
3
pages
403 - 415
publisher
Future Medicine Ltd.
external identifiers
  • wos:000175888400017
  • scopus:0036262111
ISSN
1462-2416
DOI
10.1517/14622416.3.3.403
language
English
LU publication?
yes
id
356bf750-9ddf-4ff4-88b7-59eae32c8f59 (old id 336182)
date added to LUP
2007-11-12 11:19:26
date last changed
2017-05-28 03:37:39
@article{356bf750-9ddf-4ff4-88b7-59eae32c8f59,
  abstract     = {Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods will influence the results and their interpretation, therefore it is important to be familiar with each method, its scope and limitations. Here, methods with special reference to applications for pharmacogenomics are reviewed.},
  author       = {Ringnér, Markus and Peterson, Carsten and Khan, J},
  issn         = {1462-2416},
  keyword      = {machine learning,genes,drug targets,DNA chip,diagnostic prediction,diagnostic classification,artificial neural networks,bioinformatics,microarray,support vector machines,target identification},
  language     = {eng},
  number       = {3},
  pages        = {403--415},
  publisher    = {Future Medicine Ltd.},
  series       = {Pharmacogenomics},
  title        = {Analyzing array data using supervised methods},
  url          = {http://dx.doi.org/10.1517/14622416.3.3.403},
  volume       = {3},
  year         = {2002},
}