Experiments
Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).
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Has reproduction · 67
Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.
PMID 37847658 · PMC10628388 · Bioinformatics (Oxford, England) · 2023 · 8 claims · 6 setups
PSPred-ALE outperforms state-of-the-art SARS-CoV-2 phosphorylation site predictors (e.g. DeepIPs) and handcrafted feature-based methods in benchmarking comparisons
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Has reproduction · 71
Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage.
PMID 38295140 · PMC10830027 · PloS one · 2024 · 6 claims · 7 setups
Potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways.
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Has reproduction
Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19.
PMID 40170038 · PMC11963311 · BMC medical genomics · 2025 · 7 claims · 7 setups
Differential DNA methylation occurs predominantly in intergenic regions and low methylated regions (LMRs), highlighting the role of distal regulatory elements in COVID-19 severity.
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Has reproduction · 90
Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection.
PMID 35464437 · PMC9019466 · Frontiers in immunology · 2022 · 8 claims · 8 setups
Differential polyadenylation occurs in infected Calu-3 and Vero cells at a late time point (48 hpi)
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Has reproduction · 32
Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.
PMID 34307679 · PMC8272456 · BioMed research international · 2021 · 7 claims · 2 setups
A pipeline combining Boruta and mRMR feature selection with incremental feature selection (IFS) was used to identify COVID-19-specific transcriptomic biomarkers from blood gene expression data.
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Has reproduction · 44
Dynamic Gene Attention Focus (DyGAF): Enhancing Biomarker Identification Through Dual-Model Attention Networks.
PMID 40160891 · PMC11951896 · Bioinformatics and biology insights · 2025 · 6 claims · 5 setups
DyGAF, a dual-model attention neural network (independent Model A + dependent Model B), identifies and ranks genes by significance for COVID-19 biomarker discovery more effectively than differential expression analysis (DEA) and random forest (RF) feature selection