Finding new indications of marked drugs (drug repurposing) is an appealing response for new epidemics. It can provide treatments with low costs and short development times until effective new therapies are available. In the context of COVID-19, different computational approaches were proposed to assist drug repurposing. In particular, network-based approaches are promising and aim to select drugs that reduce infection/replication by perturbing the set of human proteins used by SARS-CoV-2 (host proteins) to replicate itself and infect the human cell. In this seminar, I show some preliminary results on extending this idea for other viruses by using kernels on graphs. In addition, I show that combining network-based approaches with gene expression data of infected cell lines is promising.
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Suzana Santos - Since 2020, I am a postdoc fellow at FGV-EMAp under supervision of Prof. Alberto Paccanaro. I obtained my degrees of bachelor (2012), master (2015), and PhD (2020) in computer science at the Institute of Mathematics and Statistics, University of São Paulo. During my masters, I developed a statistical test to compare gene co-expression networks. I won the third place at the XXIII Latin American Master's Thesis Contest - CLEI 2016, and the best poster of Software and development, at the X-meeting 2015. Later, in my doctorate, I did a research internship at the Laboratory of Probability, Statistics and Modeling, Sorbonne University. In my thesis, I investigated properties of a parameter estimation procedure for random graphs based on the spectral density. I also studied the largest eigenvalue of random graphs and applied it to the analysis of brain functional networks. I am currently working with computational biology problems. In particular, I am developing network medicine and machine learning approaches for drug repositioning for viral diseases.