[talks] M Homilius general exam

Melissa M. Lawson mml at CS.Princeton.EDU
Fri May 10 13:46:03 EDT 2013

Max Homilius will present his research seminar/general exam on Friday March 17 at 10AM 
in Room 302 (note room!).  The members of his committee are:  Olga Troyanskaya (advisor), 
Mona Singh, and Tom Funkhouser.  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.

Classical pharmacological approaches for the identification of new
drug candidates often follow a one disease - one target - one drug
paradigm. At the same time, there is little knowledge about the
specific mode of action for many compounds and the principles
underlying interactions between multiple drugs.
I developed a machine learning approach for the prediction of
synergistic drug pairs for fungicidal and anti-cancer compounds. This
approach takes into account the effects of a compound on multiple
target genes that are part of complex biological network, and combines
experimental results from different large-scale studies to improve
In addition, I constructed functional networks specific to
non-steroidal anti-inflammatory drugs. Measures of differential
expression and coexpression of genes after drug treatment were
reconciled with these networks and are predictive of the mode of
action of the administered drug.

Reading list

[1] Ethem Alpaydin (2004). Introduction to Machine Learning. First
edition. MIT Press.
  Ch 1 Introduction
  Ch 2 Supervised Learning
  Ch 3 Bayesian Decision Theory
  Ch 4 Parametric Methods
  Ch 5 Multivariate Methods
  Ch 8 Nonparametric Methods
  Ch 10 Linear Discrimination
  Ch 14 Assessing and Comparing Classification Algorithms
  Ch 15 Combining Multiple Learners

[2] Aryee, M. J., Gutiérrez-Pabello, J. a, Kramnik, I., Maiti, T., &
Quackenbush, J. (2009). An improved empirical bayes approach to
estimating differential gene expression in microarray time-course
data: BETR (Bayesian Estimation of Temporal Regulation). BMC
bioinformatics, 10, 409.

[3] Cokol, M., Chua, H. N., Tasan, M., Mutlu, B., Weinstein, Z. B.,
Suzuki, Y., Nergiz, M. E., et al. (2011). Systematic exploration of
synergistic drug pairs. Molecular systems biology, 7(544), 544.

[4] Dawson, J. a, & Kendziorski, C. (2012). An empirical Bayesian
approach for identifying differential coexpression in high-throughput
experiments. Biometrics, 68(2), 455–65.

[5] Grosser, T., Fries, S., & FitzGerald, G. (2006). Biological basis
for the cardiovascular consequences of COX-2 inhibition: therapeutic
challenges and opportunities. Journal of Clinical Investigation,

[6] Jansen, G., Lee, A. Y., Epp, E., Fredette, A., Surprenant, J.,
Harcus, D., Scott, M., et al. (2009). Chemogenomic profiling predicts
antifungal synergies. Molecular systems biology, 5(338), 338.

[7] Laenen, G., Thorrez, L., Börnigen, D., & Moreau, Y. (2013).
Finding the targets of a drug by integration of gene expression data
with a protein interaction network. Molecular BioSystems.

[8] Nitsch, D., Gonçalves, J. P., Ojeda, F., De Moor, B., & Moreau, Y.
(2010). Candidate gene prioritization by network analysis of
differential expression using machine learning approaches. BMC
bioinformatics, 11(2007), 460.

[9] Poirel, C. L., Rahman, A., Rodrigues, R. R., Krishnan, A., Addesa,
J. R., & Murali, T. M. (2013). Reconciling differential gene
expression data with molecular interaction networks. Bioinformatics,
29(5), 622–9.

[10] Wong, A. K., Park, C. Y., Greene, C. S., Bongo, L. a, Guan, Y., &
Troyanskaya, O. G. (2012). IMP: a multi-species functional genomics
portal for integration, visualization and prediction of protein
functions and networks. Nucleic acids research, 40, W484–90.

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