Abstract:

The rapid development of biological has generated a huge amount of data, which provides a processing as well as a global view of gene expression levels across different conditions and over multiple stages. One of the most important steps is to use clustering approaches to extract the useful and rational patterns of gene expression. We hope to find the relationships between the patterns and real functions of the genes. In this talk, I will introduce some of my work in clustering gene data. I will show some initial results in this direction and also discuss possible future work.

Related Readings:

K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data Z. Bar-Joseph, E.D. Demaine, D.K. Gifford, A.M. Hamel, T.S. Jaakkola, N.H. Srebro In Bioinformatics, 2002 Molecular Classification of Multiple Tumor Types C. Yeang, S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, M. Angelo, M. Reich, E. Lander, J. Mesirov, T. Golub In Proceedings of Ninth International Conference on Intelligent Systems in Molecular Biology, 2001 A common dataset used in micro-array clustering: http://psrg.lcs.mit.edu/~zivbj/ismb01/optimal.html