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. ---------------------- Abstract ======== 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 predictions. 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, 116(1). [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.