On Clustering Biological Data Using Unsupervised and Semi-Supervised Message Passing

Authors:
Huimin Geng;Xutao Deng;Dhundy Bastola;Hesham Ali
Affiliations:
University of Nebraska Medical Center;University of Nebraska at Omaha;University of Nebraska Medical Center;University of Nebraska at Omaha
Venue:
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Year:
2005

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Abstract

Noticing that unsupervised clustering may produce clusters that are irrelevant to the research hypotheses and interests, we generalize traditional unsupervised clustering into semi-supervised clustering based on our previously proposed Message Passing Clustering (MPC). In the semi-supervised MPC, prior knowledge such as instance-level and attribute-level constraints are used to guide the clustering process towards better and interpretable partitions. We applied the unsupervised MPC (null background) to phylogenetic analysis of Mycobacterium and the semi-supervised MPC to colon cancer microarray data analysis. The results show that MPC is superior to the widely accepted neighbor-joining and hierarchical clustering methods, and the semi-supervised MPC is even more powerful in biological data analysis such as gene selection and cancer diagnosis using microarray.