[talks] A Pop general exam

Melissa Lawson mml at CS.Princeton.EDU
Thu May 14 10:30:38 EDT 2009

Ana Pop will present her research seminar/general exam on Wednesday May 20 at 
10AM in room 402.  The members of her committee are:  Olga Troyanskaya, advisor, 
David Blei, and Rob Schapire.  Everyone is invited to attend her talk, and those 
faculty wishing to remain for the oral exam following are welcome to do so.  Her 
abstract and reading list follow below.

------ Abstract: ------

The availability of genome-scale data has enabled gene function prediction
for different organisms. Computational approaches to this problem have aimed
to integrate all this heterogeneous information. One method that has been
applied with great success has been Bayesian data integration, which, given
the genomic data and known functional relationships, can predict other
functional relationships and organize the genomic information into
functional relationship networks. In addition to understanding the function
of genes we also aim to understand the progression of these genes' functions
and interactions as the organism develops.

Computational approaches have been applied mostly to simple organisms such
as the very well-studied Saccharomyces cerevisiae (baker's yeast). More
complex organisms, such as Arabidopsis thaliana (the model organism for
plants), which is the focus of this project, are much more challenging to
analyze as they are substantially more complex and have different tissues
types and development stages. Therefore, this plant is ideal for a
developmental gene functional networks study.

We have created a global functional network for Arabidopsis where nodes are
genes and links between the nodes represent a functional relationship (the
link weight represents the probability of interaction). Analysis of this
network showed that the top few hundred highest scoring functional
relationships are between genes related to photosynthesis. In addition to
the global network, we also have biological process context-specific
networks for Arabidopsis, which shows gene functional relationships in
different biological process contexts (such as cell cycle or DNA

We are also extending this model to determine developmental progression of
gene function both globally and in various development-stage contexts. We
expect that overlaying the biological process context-specific networks with
the development stage context-specific networks will shed light on the way
gene function progresses over the different developmental stages of the

------ Reading List: ------

[Book] Artificial Intelligence: A Modern Approach (Second Edition) by Stuart
Russell and Peter Norvig
     - section V (Uncertain knowledge and reasoning)
     - section VI (Learning)

[1] C Huttenhower, O Troyanskaya; Bayesian data integration: a functional
perspective; Comput. Syst. Bioinformatics, 2006 

[2] Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, Troyanskaya OG:
Predicting gene function in a hierarchical context with an ensemble of
classifiers. Genome Biol 2008, 9(suppl 1):S3.

[3] Curtis Huttenhower, Erin M. Haley, Matthew A. Hibbs, Vanessa Dumeaux,
Daniel R. Barrett, Hilary A. Coller, and Olga G. Troyanskaya. Exploring the
human genome with functional maps, Genome Research 2009

[4] S Rogers, M Girolami, A Bayesian regression approach to the inference of
regulatory networks from gene expression data., Bioinformatics, Vol. 21, No.
14. (15 July 2005), pp. 3131-3137

[5] Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy,
Learning gene functional classifications from multiple data types, Journal
of Computational Biology. 9(2):401-411, 2002

[6] Quackenbush J., Computational analysis of microarray data., Nat Rev
Genet. 2001 Jun;2(6):418-27

[7] Brendan J. Frey and Delbert Dueck, Clustering by Passing Messages
Between Data Points, (11 January 2007) Science

[8] Alter O, Brown PO, Botstein D (2000) Singular value decomposition for
genomewide expression data processing and modeling. Proc Natl Acad Sci USA

[9] Boyes et al., 2001 D.C. Boyes, A.M. Zayed, R. Ascenzi, A.J. McCaskill,
N.E. Hoffman and K.R. Davis, Growth stage-based phenotypic analysis of
Arabidopsis: a model for high throughput functional genomics in plants,
Plant Cell 13 (2001), pp. 1499-1510.

[10] S.M. Brady et. al. A high-resolution root spatiotemporal map reveals
dominant expression patterns.
Science 318 (2007), 801-806

[11] Anjali Iyer-Pascuzzi, June Simpson, Luis Herrera-Estrella, Philip N
Benfey, Functional genomics of Arabidopsis root functional genomics, Current
Opinion in Plant Biology, In Press, Corrected Proof, Available online 29
December 2008

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