[talks] Max Homilius will present his FPO, "Network-Based Prioritization of Disease Genes, Animal Models, and Drug Targets" on Friday, 8/24/2018 at 10am, 280 Lewis-Sigler Institute.

[Nicki Gotsis]

Max Homilius will present his FPO, "Network-Based Prioritization of Disease Genes, Animal Models, and Drug Targets" on Friday, 8/24/2018 at 10am, 280 Lewis-Sigler Institute. 

The members of his committee are as follows: Readers: Barbara Engelhardt and Tilo Grosser (UPenn); Examiners: Mona Singh, Coleen Murphy (MOL), and Olga Troyanskaya (adviser). 


A copy of his thesis is available upon request. 

Everyone is invited to attend his talk. The talk abstract follows below. 




In living organisms, biomolecules interact in complex molecular networks that underlie 

cell function and whose dysregulation leads to disease. These networks thus 

provide a key lens for understanding the molecular basis of human disease as well 

as treatment development. In this thesis, I develop three network-based computational 

approaches for human disease research and gaining insight into the mode of 

action of drugs. At their core, these methods employ functional interaction networks, 

which provide a genome-wide view of biochemical and pathway-level interactions and 

summarize essential functional information derived from diverse and heterogeneous 

functional genomics experiments. 

First, I propose a network-based method that can detect critical genes and pathways 

targeted by a drug treatment from gene expression data even in the absence 

of large-scale expression di↵erences. This approach enables the analysis of low-dose 

drug screens, ranking potential targets and drug-perturbed biological processes with 

higher accuracy than prior network-based methods or gene-expression data alone. 

Furthermore, I present a method that by inferring and comparing genome-wide profiles 

for human diseases and animal model phenotypes identifies analogous disease 

models with high accuracy and more robustly than prior methods relying on shared 

gene content. This method allows to aggregate the wealth of existing model organism 

knowledge across multiple species and to identify related phenotypes and novel homologous 

genes of human diseases for experimental follow-up. Lastly, by constructing 

a joint tissue-specific classifier for human disease genes, we can significantly improve 

the prediction of associated genes for rare human diseases. This neural network-based 

approach makes use of a functional network embedding leveraging tissue-specific expression 

data and model organism phenotype information in a multi-label classification 

setting. Overall, the methods I developed provide data-driven, molecular-level 

solutions to major biological challenges relevant to human health. 
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