A network-based algorithm for drug repurposing and its application to COVID-19.
Martedì, 30 November, 2021 - 19:15
Aula A2 (DIAG)
Prof.ssa Paola Paci (firstname.lastname@example.org)
Seminario di Giulia Fiscon, vincitrice procedura selettiva RTD-A per il SSD ING-INF/06.
In ottemperanza ai requisiti previsti dalla procedura selettiva di cui al bando 5/2021 del 12/10/2021, per il reclutamento di n. 1 ricercatore con rapporto di lavoro a tempo determinato di tipologia “A”, con regime di impegno a tempo pieno, settore scientifico- ING-INF/06, settore concorsuale 09/G2, presso il Dipartimento di Ingegneria Informatica Automatica e Gestionale “Antonio Ruberti”, venerdi 3/12/2021 alle ore 15.00 in aula A2 si terrà il seminario di Giulia Fiscon, vincitrice della procedura, che illustrerà le sua attività di ricerca svolta e in corso di svolgimento.
Il seminario sarà anche trasmesso in modalità telematica su Zoom.
Per partecipare da remoto connettersi all'indirizzo seguente: https://uniroma1.zoom.us/j/81834006054?pwd=N3FBSEtKRHBkVnhiY2xtQVd0V1VvUT09
(Meeting ID: 818 3400 6054, Passcode: 702707)
The seminary will be about the main research activity carried out by Giulia Fiscon in the last years regarding Network Medicine and Drug Repositioning (DR). DR is the process of reusing an ‘old drug’ for new therapeutic purposes and appears as a powerful solution for emerging diseases, such as COVID-19, since it allows to shorten the time and reduce the cost compared to de novo drug discovery. In this context, this seminary will cover the presentation of a network-based tool for drug repurposing called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), developed with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods. SAveRUNNER was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worthy of further investigation.
Giulia Fiscon holds a degree summa cum laude in Biomedical Engineering at Campus Bio-Medico University of Rome in 2012 and a PhD in Engineering in Computer Science at Department of Computer, Control and Management Engineering of Sapienza University of Rome in 2016. From the end of 2015 to Sept 2020, she was a post-doctoral researcher in the bioinformatics and system biology group of the Institute for Systems Analysis and Computer Science “A. Ruberti” (IASI) of National Research Council (CNR) in Rome. In last year, she was a research collaborator at Foundation for Personalized Medicine (FMP) and a post-doctoral researcher at the Department of Computer, Control and Management Engineering of Sapienza University of Rome. Her research interests are in the area of biological complex system study. In particular, she works in the field of bioinformatics, computational biology, and network medicine.
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