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Version 6 2020-11-20, 13:51
Version 5 2020-09-15, 14:08
Version 4 2020-09-15, 14:04
Version 3 2020-09-15, 13:57
Version 2 2020-06-25, 19:45
Version 1 2020-06-25, 13:33
posted on 2020-09-15, 14:08 authored by nina verstraete, Giulia Bertagnolli, Arsham GhavasiehArsham Ghavasieh, Vera Pancaldi, Giuseppe JurmanGiuseppe Jurman, Manlio De domenicoManlio De domenico
CovMulNet19 is a comprehensive network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Extensive network analysis methods, based on a bootstrap approach, allow us to prioritise a list of diseases that display a high similarity to \covid and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for CoVid19-related molecular processes. We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients.



National Health and Medical Research Council

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Foundation Toulouse Cancer Santé

BioInfo4Women programme at the BarcelonaSupercomputing Cente

PierreFabre Research Institute as part of the Chair of Bioinformatics in Oncologyof the CRCT