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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Saudi Arabian Y-Chromosome diversity and its relationship with nearby regions.
PMID 19772609 · PMC2759955 · BMC genetics · 2009 · 8 claims · 5 setups
Saudi Arabia differs from other Arabian Peninsula countries by a significantly higher presence of J2-M172 lineages.
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An SVM-based system for predicting protein subnuclear localizations.
PMID 16336650 · PMC1325059 · BMC bioinformatics · 2005 · 7 claims · 3 setups
New kernels defined on k-peptide vectors mapped by BLOSUM62-based high-scored pair matrices (D1, D2, D3) improve SVM discrimination of protein subnuclear localization compared to conventional k-peptide encodings.
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A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145.
PMID 17939873 · PMC2222656 · BMC bioinformatics · 2007 · 8 claims · 4 setups
Yu et al.'s original comparison ran RJaCGH's MCMC sampler for a severely insufficient number of iterations (50 burn-in, 500 total)
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Complex germline and somatic mutation processes at a haploid human minisatellite shown by single-molecule analysis.
PMID 18929582 · PMC2599865 · Mutation research · 2008 · 8 claims · 5 setups
Overall MSY1 mutation frequencies in sperm (2.68%) and blood (1.88%) are not significantly different
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Automatic discovery of cross-family sequence features associated with protein function.
PMID 16409628 · PMC1395344 · BMC bioinformatics · 2006 · 8 claims · 6 setups
A self-supervised data mining approach can find relationships between sequence features and functional annotations without preconceived functional categories.
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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 · 7 claims · 3 setups
PSPred-ALE, a deep learning predictor using a self-adaptive learning embedding algorithm, automatically extracts contextual sequence features and identifies SARS-CoV-2 phosphorylation sites without feature engineering.