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 · 68
Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing.
PMID 31443728 · PMC6708158 · Genome medicine · 2019 · 6 claims · 8 setups
A near-perfect chronological age predictor can in principle be developed from DNA methylation when training sample size is sufficiently large.
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Has reproduction · 75
ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method.
PMID 38247911 · PMC10813502 · Bioengineering (Basel, Switzerland) · 2023 · 8 claims · 4 setups
ResnetAge, a ResNet-based neural network using 22,278 shared Illumina 27K/450K CpG sites, predicts DNA methylation age from beta values.
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Has reproduction · 83
Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer.
PMID 38066424 · PMC10709847 · BMC bioinformatics · 2023 · 8 claims · 8 setups
CHCT, a two-tier hierarchical classification tool built from methylation data, accurately classifies primary cancer type across 30 cancer types.
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Has reproduction · 83
Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.
PMID 39856156 · PMC11760357 · Scientific reports · 2025 · 8 claims · 8 setups
Ten multiomics clustering algorithms identify two distinct lactylation cancer subtypes (CS1 and CS2) in LUAD
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Has reproduction · 32
Developing prognostic gene panel of survival time in lung adenocarcinoma patients using machine learning.
PMID 35117753 · PMC8799101 · Translational cancer research · 2020 · 8 claims · 5 setups
Naïve Bayes using a 22-gene panel is the best-performing and most stable machine learning model for predicting LUAD survival time (>3 vs <3 years)
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Has reproduction · 70
Predicting enhancers in mammalian genomes using supervised hidden Markov models.
PMID 30917778 · PMC6437899 · BMC bioinformatics · 2019 · 8 claims · 8 setups
eHMM predicts enhancers with high precision and recall comparable to state-of-the-art methods and consistently outperforms them in accuracy and resolution