[talks] C Park general exam
mml at CS.Princeton.EDU
Thu May 7 16:33:52 EDT 2009
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.
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.
 Pattern Recognition and Machine Learning. by C. M. Bishop.
Chapter 1~4, 6~9, 12~14
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
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
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,
BMC Bioinformatics. 2006 Mar 7;7:113.
More information about the talks