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).

• Machine learning-predicted chromatin organization landscape across pediatric tumors.

  PMID 41904260 · PMC13039956 · Scientific reports · 2026 · 8 claims · 5 setups

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  SuPreMo-Akita (built on the Akita CNN) enables systematic in silico prediction of somatic SV effects on 3D genome folding across large SV cohorts where experimental testing is infeasible

  somatic per-sample tumor paired mutagenesis dimensionality h3k27ac

• Biomarkers that discriminate multiple myeloma patients with or without skeletal involvement detected using SELDI-TOF mass spectrometry and statistical and machine learning tools.

  PMID 17124346 · PMC3862287 · Disease markers · 2006 · 8 claims · 5 setups

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  SELDI-TOF MS serum profiling can discriminate MM patients with vs without skeletal (bone lesion) involvement using peak biomarkers

  serum seldi seldi-tof serum univariate