ReviewBriefings in bioinformatics2021
A review of COVID-19 biomarkers and drug targets: resources and tools.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The potential of circulating microRNAs as novel diagnostic biomarkers of COVID-19: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Impact of COVID-19 on inflammatory disease research: a literature analysis of 77,575 PubMed articles.Reumatologia · 2026Article
- Challenges and future directions of AIRR-seq-based diagnostics.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Neopterin production in relation to COVID-19 in the Haut-Ogooué Province, Gabon.BMC infectious diseases · 2024Article
- Preparation of noninfectious scRNAseq samples from SARS-CoV-2-infected epithelial cells.PloS one · 2023Article
- Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort.Journal of clinical medicine · 2022Article
- Significance of Cardiac Troponins as an Identification Tool in COVID-19 Patients Using Biosensors: An Update.Frontiers in molecular biosciences · 2022Review
- Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review.Robotics and autonomous systems · 2021Review
- Single-cell transcriptome identifiesiScience · 2021Article
- COVID-19 pandemic and artificial intelligence possibilities: A healthcare perspective.Medical journal, Armed Forces India · 2021Article
- The Role of Interleukin-8 in Lung Inflammation and Injury: Implications for the Management of COVID-19 and Hyperinflammatory Acute Respiratory Distress Syndrome.Frontiers in pharmacology · 2021Review
- Genome-Wide Profiling Reveals Alternative Polyadenylation of Innate Immune-Related mRNA in Patients With COVID-19.Frontiers in immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.
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Identifiers
What OpenQuestion holds
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.