Knowledge guided analysis of microarray data

Authors:
Zhuo Fang;Jiong Yang;Yixue Li;Qingming Luo;Lei Liu
Affiliations:
Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, Hubei, PR China;Department of EECS, Case Western Reserve University, Cleveland, OH;Shanghai Center for Bioinformatics Technology, Shanghai, PR China;Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, Hubei, PR China;Shanghai Center for Bioinformatics Technology, Shanghai, PR China and W.M. Keck Center for Comparative and Functional Genomics, University of Illinois at Urbana-Champaign, W. Gregory, IL
Venue:
Journal of Biomedical Informatics
Year:
2006

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Abstract

To microarray expression data analysis, it is well accepted that biological knowledge-guided clustering techniques show more advantages than pure mathematical techniques. In this paper, Gene Ontology is introduced to guide the clustering process, and thus a new algorithm capturing both expression pattern similarities and biological function similarities is developed. Our algorithm was validated on two well-known public data sets and the results were compared with some previous works. It is shown that our method has advantages in both the quality of clusters and the precision of biological annotations. Furthermore, the clustering results can be adjusted according to different stringency requirements. It is expected that our algorithm can be extended to other biological knowledge, for example, metabolic networks.