ArticleFrontiers in cell and developmental biology2020
Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Differential Inflammatory and Immune Response to Viral Infection in the Upper-Airway and Peripheral Blood of Mild COVID-19 Cases.Journal of personalized medicine · 2024Article
- scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis.Genome biology · 2024Article
- The Immune Response ofInternational journal of molecular sciences · 2024Article
- 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
- Article
- Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation.Journal of translational medicine · 2023Article
- Epigenomic landscape exhibits interferon signaling suppression in the patient of myocarditis after BNT162b2 vaccination.Scientific reports · 2023Article
- Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach.Biochemical genetics · 2023Article
- Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.Computers in biology and medicine · 2023Article
- SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach.SN computer science · 2023Article
- Differential expression of antiviral and immune-related genes in individuals with COVID-19 asymptomatic or with mild symptoms.Frontiers in cellular and infection microbiology · 2023Article
- Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.Frontiers in genetics · 2023Article
- Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.BioMed research international · 2023Article
- An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Screening gene signatures for clinical response subtypes of lung transplantation.Molecular genetics and genomics : MGG · 2022Article
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
- Enrichment analysis on regulatory subspaces: A novel direction for the superior description of cellular responses to SARS-CoV-2.Computers in biology and medicine · 2022Article
- Does educational stress mediate the relationship between intolerance of uncertainty and academic life satisfaction in teenagers during the COVID-19 pandemic?Psychology in the schools · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
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