Finding rule groups to classify high dimensional gene expression datasets

An, Jiyuan and Chen, Yi-Ping Phoebe 2009, Finding rule groups to classify high dimensional gene expression datasets, Computational Biology and Chemistry, vol. 33, no. 1, pp. 108-113, doi: 10.1016/j.compbiolchem.2008.07.031.

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Title Finding rule groups to classify high dimensional gene expression datasets
Alternative title Brief communication : Finding rule groups to classify high dimensional gene expression datasets
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Journal name Computational Biology and Chemistry
Volume number 33
Issue number 1
Start page 108
End page 113
Total pages 6
Publisher Elsevier Ltd
Place of publication Oxford, England
Publication date 2009-02
ISSN 1476-9271
Keyword(s) Gene expression datasets
Microarray data analysis
Summary Microarray data provides quantitative information about the transcription profile of cells. To analyse microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.
Language eng
DOI 10.1016/j.compbiolchem.2008.07.031
Field of Research 080106 Image Processing
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2009
Copyright notice ©2008, Elsevier Ltd.
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