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 · 74
Evaluation of classification and forecasting methods on time series gene expression data.
PMID 33156855 · PMC7647064 · PloS one · 2020 · 8 claims · 4 setups
Deep learning based methods generally outperform traditional approaches for time series gene expression classification.
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Has reproduction · 94
Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.
PMID 35794110 · PMC9258765 · Nature communications · 2022 · 8 claims · 5 setups
Deep learning (FFNN-SS and CNN-CBLV) enables accurate and fast likelihood-free estimation of epidemiological parameters and model selection from phylogenies
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How negative sampling shapes the performance of transcription factor binding site prediction models.
PMID 41601205 · PMC12910371 · Bioinformatics (Oxford, England) · 2026 · 7 claims · 5 setups
Negative sampling technique significantly impacts TFBS prediction model performance and interpretation of results
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AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.
PMID 41792137 · PMC13087051 · Nature communications · 2026 · 7 claims · 8 setups
AMR-GNN, a graph neural network integrating multiple genomic representations (unitigs, SNPs, FCGR) via low-rank multimodal fusion, improves AMR phenotype prediction in P. aeruginosa compared to single-representation baseline models.
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Has reproduction · 50
DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.
PMID 33662629 · PMC9801043 · Genomics, proteomics & bioinformatics · 2022 · 8 claims · 8 setups
DeeReCT-APA quantitatively predicts the usage of all competing PASs of a gene simultaneously, rather than casting the problem as pairwise comparison like prior methods.