{"entity": "publication", "iuid": "1291a8b321634882844a3e6520ab3515", "timestamp": "2026-09-09T10:38:14.031Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1291a8b321634882844a3e6520ab3515.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1291a8b321634882844a3e6520ab3515"}}, "title": "Using informative features in machine learning based method for COVID-19 drug repurposing.", "authors": [{"family": "Aghdam", "given": "Rosa", "initials": "R", "orcid": "0000-0001-9045-9592", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0179bdcc8b8e4bd68549403ed5b66b79.json"}}, {"family": "Habibi", "given": "Mahnaz", "initials": "M", "orcid": "0000-0002-8969-2706", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f04af4e059814b78bfb107c3a5782f70.json"}}, {"family": "Taheri", "given": "Golnaz", "initials": "G", "orcid": "0000-0002-2741-0355", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/014c217121d346b2b371bbc1c2fede57.json"}}], "type": "journal article", "published": "2021-09-20", "journal": {"title": "J Cheminform", "issn": "1758-2946", "issn-l": "1758-2946", "volume": "13", "issue": "1", "pages": "70"}, "abstract": "Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.", "doi": "10.1186/s13321-021-00553-9", "pmid": "34544500", "labels": {"Golnaz Taheri": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8451172"}, {"db": "pii", "key": "10.1186/s13321-021-00553-9"}], "notes": [], "created": "2025-03-21T09:08:40.624Z", "modified": "2025-03-21T10:36:00.374Z"}