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Multiclass discovery in array data

Liu, Yingchun LU and Ringnér, Markus LU (2004) In BMC Bioinformatics 5.
Abstract
Background

A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.





Results

We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method... (More)
Background

A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.





Results

We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method allows for discovery of more than two classes. We applied our method on two published microarray data sets: small round blue cell tumors and breast tumors. The method predicts relevant classes in the data sets with high success rates.





Conclusions

We conclude that the proposed method is accurate and efficient in finding biologically relevant classes in microarray data. Additionally, the method is useful for quality control of microarray experiments. We have made the method available as a computer program. (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
BMC Bioinformatics
volume
5
publisher
BioMed Central
external identifiers
  • PMID:15180908
  • WOS:000222505300002
  • Scopus:13244292283
ISSN
1471-2105
DOI
10.1186/1471-2105-5-70
language
English
LU publication?
yes
id
2d324207-5ee6-43e3-b2b4-956762695c23 (old id 796344)
date added to LUP
2007-12-27 19:05:04
date last changed
2017-01-01 07:43:09
@article{2d324207-5ee6-43e3-b2b4-956762695c23,
  abstract     = {Background<br/><br>
A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.<br/><br>
<br/><br>
<br/><br>
Results<br/><br>
We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method allows for discovery of more than two classes. We applied our method on two published microarray data sets: small round blue cell tumors and breast tumors. The method predicts relevant classes in the data sets with high success rates.<br/><br>
<br/><br>
<br/><br>
Conclusions<br/><br>
We conclude that the proposed method is accurate and efficient in finding biologically relevant classes in microarray data. Additionally, the method is useful for quality control of microarray experiments. We have made the method available as a computer program.},
  articleno    = {70},
  author       = {Liu, Yingchun and Ringnér, Markus},
  issn         = {1471-2105},
  language     = {eng},
  publisher    = {BioMed Central},
  series       = {BMC Bioinformatics},
  title        = {Multiclass discovery in array data},
  url          = {http://dx.doi.org/10.1186/1471-2105-5-70},
  volume       = {5},
  year         = {2004},
}