ArticleBioMed research international2020
Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis.BMC pulmonary medicine · 2024Article
- Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.Scientific reports · 2024Article
- PWN: enhanced random walk on a warped network for disease target prioritization.BMC bioinformatics · 2023Article
- Network analysis of atherosclerotic genes elucidates druggable targets.BMC medical genomics · 2022Article
- Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.BioMed research international · 2022Article
- Prolonged viral shedding prediction on non-hospitalized, uncomplicated SARS-CoV-2 patients using their transcriptome data.Computer methods and programs in biomedicine update · 2022Article
- Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.GigaScience · 2021Article
- Interplay between SARS-CoV-2-derived miRNAs, immune system, vitamin D pathway and respiratory system.Journal of cellular and molecular medicine · 2021Article
- Article
- Consequences of coronavirus infections for primitive and mature hematopoietic cells: new insights and why it matters.Current opinion in hematology · 2021Review
- Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection.Frontiers in immunology · 2021Article
- Identification of Novel Choroidal Neovascularization-Related Genes Using Laplacian Heat Diffusion Algorithm.BioMed research international · 2021Article
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6 authors.
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Abstract
Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus-human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.
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