Chris Park will present his research seminar/general exam on Wednesday May 13 at 10AM in Room 402. The members of his committee are: Olga Troyanskaya, advisor, Rob Schapire, and David Blei. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. ---------------------------------------- Abstract Inferring heterogeneous biological networks from heterogeneous data. With the advancement of quantitative biology, there has been a dramatic increase in functional genomic data. Using these heterogeneous genomic data which includes protein-protein interaction, gene expression, localization, and gene knockout studies has enabled researchers to construct functional gene networks. In functional gene networks, each edge represents the probabilistic support for a functional relationship between two genes. However in practice, the goal of biology is to understand further depth of the relationship between genes, beyond the abstract notion of a functional relation. In this work, we expand the depth and breadth of functional gene networks. We use prior biological knowledge of gene interactions types to construct an interaction ontology, which enables us to predict interaction types between genes using a Bayesian hierarchical framework. Ultimately, our work provides further insight into the genetic wiring of the cell. ---------------------------------------- Reading list Book: [1] Pattern Recognition and Machine Learning. by C. M. Bishop. Chapter 1~4, 6~9, 12~14 Papers: 1. Hierarchical multi-label prediction of gene function. Barutcuoglu, Z.; Schapire, R.E.; Troyanskaya, O.G. , Bioinformatics, (2006) 2. Discovery of biological networks from diverse functional genomic data. Myers, C.L.; Robson, D.; Wible, A.; Hibbs, M.A.; Chiriac, C.; Theesfeld, C.L.; Dolinski, K.; Troyanskaya, O.G. , Genome Biology, (2005) 3. Using Bayesian networks to analyze expression data. Nir Friedman, Michal Linial, Iftach Nachman, and Dana Pe'er. In RECOMB 4, pages 127-135. ACM Press, 2000. 4. Causal protein-signaling networks derived from multiparameter single-cell data. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A. & Nolan, G. P. Science 308, 523-529 (2005). 5. Conserved patterns of protein interaction in multiple species. Sharan, R., Suthram, S., Kelley, R. M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R. M., and Ideker, T. /Proc Natl Acad Sci U S A. / *8*:102(6) 1974-79 (2005) 6. Genomic analysis of regulatory network dynamics reveals large topological changes. Luscombe, N., & et al. (2004). Nature, 431, 308. 7. Statistical significance for genome-wide studies. Storey JD and Tibshirani R. Proceedings of the National Academy of Sciences, 100: 9440-9445 (2003). 8. Cluster analysis and display of genome-wide expression patterns. Michael B. Eisen, Paul T. Spellman, Patrick O. Brown, and David Botstein PNAS, Dec (1998); 95: 14863 - 14868. 9. Genetics of global gene expression. Rockman, Kruglyak (2006). Nature Review Genetics 7:862-72. 10. An improved map of conserved regulatory sites for Saccharomyces cerevisiae. MacIsaac KD, Wang T, Gordon DB, Gifford DK, Stormo GD, Fraenkel E. BMC Bioinformatics. 2006 Mar 7;7:113.