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).

• Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.

  PMID 34307679 · PMC8272456 · BioMed research international · 2021 · 7 claims · 2 setups

  Has reproduction · 32

  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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• Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era.

  PMID 34310590 · PMC8354454 · PLoS computational biology · 2021 · 7 claims · 6 setups

  Has reproduction · 98

  Updated synthetic contact matrices were generated for 177 geographical locations covering 97.2% of the world's population (up from 152 locations/95.9% in 2017).

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• 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

  Has reproduction · 44

  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

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• Systematic identification of ACE2 expression modulators reveals cardiomyopathy as a risk factor for mortality in COVID-19 patients.

  PMID 35012625 · PMC8743438 · Genome biology · 2022 · 7 claims · 8 setups

  Has reproduction · 68

  GENEVA is a semi-automated, study-design-agnostic framework that mines large-scale public RNA-seq data to identify conditions modulating a gene of interest's expression

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• Cell-type annotation with accurate unseen cell-type identification using multiple references.

  PMID 37379341 · PMC10335708 · PLoS computational biology · 2023 · 8 claims · 4 setups

  Has reproduction · 68

  mtANN integrates multiple reference datasets and eight gene selection methods via ensemble learning (multiple deep classification models + majority voting) to improve cell-type annotation accuracy

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• Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

  PMID 40106407 · PMC11964219 · PLoS genetics · 2025 · 7 claims · 6 setups

  Has reproduction · 95

  Mouse-Geneformer, a Transformer Encoder model pre-trained via masked-token self-supervised learning on mouse-Genecorpus-20M, was successfully constructed following the original human Geneformer architecture.

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