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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Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information.
PMID 15941473 · PMC1164402 · BMC bioinformatics · 2005 · 8 claims · 5 setups
A combined linguistic+statistical+rule-based method achieves precision 0.61 and recall 0.97 (f=0.74) detecting yeast protein-protein interactions across 12,300 Medline abstracts.
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Metappuccino: large language model-driven reconstruction of sequence read archive metadata for cancer research.
PMID 42057294 · PMC13148957 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 3 setups
Metappuccino reconstructs 19 metadata classes by combining deterministic rule-based extraction/normalization (for explicit context) with LoRA-specialized Mistral-7B-Instruct completion (for missing/ambiguous fields)
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The Universal Protein Resource (UniProt) in 2010.
PMID 19843607 · PMC2808944 · Nucleic acids research · 2010 · 8 claims · 5 setups
UniProt is a centralized, freely accessible, comprehensive knowledgebase of protein sequence and functional annotation maintained by the EBI, SIB and PIR consortium.
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The Genographic Project public participation mitochondrial DNA database.
PMID 17604454 · PMC1904368 · PLoS genetics · 2007 · 7 claims · 4 setups
The Genographic Project created the largest standardized human mtDNA database to date, comprising 78,590 genotypes from the first 18 months of public participation.
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A metadata approach for clinical data management in translational genomics studies in breast cancer.
PMID 19948017 · PMC3225860 · BMC medical genomics · 2009 · 8 claims · 5 setups
A metadata/CDE-based approach using CancerGrid's semantic web tools enables automatic integration of heterogeneous clinical datasets without loss of original detail
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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.