Multiclass discovery in array data
(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:
https://lup.lub.lu.se/record/796344
- author
- Liu, Yingchun LU and Ringnér, Markus LU
- organization
- publishing date
- 2004-06-04
- type
- Contribution to journal
- publication status
- published
- subject
- in
- BMC Bioinformatics
- volume
- 5
- article number
- 70
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:15180908
- wos:000222505300002
- scopus:13244292283
- pmid:15180908
- 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
- 2016-04-04 09:08:03
- date last changed
- 2024-01-12 09:26:16
@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.}}, author = {{Liu, Yingchun and Ringnér, Markus}}, issn = {{1471-2105}}, language = {{eng}}, month = {{06}}, publisher = {{BioMed Central (BMC)}}, series = {{BMC Bioinformatics}}, title = {{Multiclass discovery in array data}}, url = {{http://dx.doi.org/10.1186/1471-2105-5-70}}, doi = {{10.1186/1471-2105-5-70}}, volume = {{5}}, year = {{2004}}, }