13th IEEE International Conference on BioInformatics and BioEngineering (2005)
Oct. 19, 2005 to Oct. 21, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BIBE.2005.44
Huimin Geng , University of Nebraska Medical Center
Xutao Deng , University of Nebraska at Omaha
Dhundy Bastola , University of Nebraska Medical Center
Hesham Ali , University of Nebraska at Omaha
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.
D. Bastola, X. Deng, H. Geng and H. Ali, "On Clustering Biological Data Using Unsupervised and Semi-Supervised Message Passing," 13th IEEE International Conference on BioInformatics and BioEngineering(BIBE), Minneapolis, Minnesota, 2005, pp. 294-298.