{"entity": "researcher", "timestamp": "2026-08-10T19:59:34.155Z", "family": "Aghdam", "given": "Rosa", "initials": "R", "orcid": "0000-0001-9045-9592", "affiliations": ["School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. rosa.aghdam@ipm.ir."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/0179bdcc8b8e4bd68549403ed5b66b79.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/0179bdcc8b8e4bd68549403ed5b66b79"}}, "publications": [{"entity": "publication", "iuid": "1291a8b321634882844a3e6520ab3515", "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"}, {"entity": "publication", "iuid": "1339105abbee4ad4b5cdb2de9e4735a3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1339105abbee4ad4b5cdb2de9e4735a3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1339105abbee4ad4b5cdb2de9e4735a3"}}, "title": "A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing.", "authors": [{"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"}}, {"family": "Aghdam", "given": "Rosa", "initials": "R", "orcid": "0000-0001-9045-9592", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0179bdcc8b8e4bd68549403ed5b66b79.json"}}], "type": "journal article", "published": "2021-04-30", "journal": {"title": "Sci Rep", "issn": "2045-2322", "issn-l": "2045-2322", "volume": "11", "issue": "1", "pages": "9378"}, "abstract": "The Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus needs a fast recognition of effective drugs to save lives. In the COVID-19 situation, finding targets for drug repurposing can be an effective way to present new fast treatments. We have designed a two-step solution to address this approach. In the first step, we identify essential proteins from virus targets or their associated modules in human cells as possible drug target candidates. For this purpose, we apply two different algorithms to detect some candidate sets of proteins with a minimum size that drive a significant disruption in the COVID-19 related biological networks. We evaluate the resulted candidate proteins sets with three groups of drugs namely Covid-Drug, Clinical-Drug, and All-Drug. The obtained candidate proteins sets approve 16 drugs out of 18 in the Covid-Drug, 273 drugs out of 328 in the Clinical-Drug, and a large number of drugs in the All-Drug. In the second step, we study COVID-19 associated proteins sets and recognize proteins that are essential to disease pathology. This analysis is performed using DAVID to show and compare essential proteins that are contributed between the COVID-19 comorbidities. Our results for shared proteins show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases.", "doi": "10.1038/s41598-021-88427-w", "pmid": "33931664", "labels": {"Golnaz Taheri": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8087682"}, {"db": "pii", "key": "10.1038/s41598-021-88427-w"}], "notes": [], "created": "2025-03-21T09:11:21.991Z", "modified": "2025-03-21T09:32:33.581Z"}]}