ArticlePatterns (New York, N.Y.)2021
Network-based virus-host interaction prediction with application to SARS-CoV-2.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.Briefings in bioinformatics · 2024Article
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network.Briefings in bioinformatics · 2022Article
- Prediction of viral-host interactions of COVID-19 by computational methods.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.Patterns (New York, N.Y.) · 2022Article
- SARS-CoV-2 host prediction based on virus-host genetic features.Scientific reports · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis.Scientific reports · 2021Article
- Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.