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 · 94
Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer.
PMID 33729159 · PMC8012062 · eLife · 2021 · 7 claims · 6 setups
Discrete (Boolean/Ising) and continuous (RACIPE) simulations of the SCLC regulatory network yield similar multistable phenotypic distributions, with four dominant steady states (X1-X4) that map onto experimentally observed SCLC molecular subtypes.
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Full-text index only
Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data.
PMID 19997482 · PMC2776985 · PLoS computational biology · 2009 · 7 claims · 4 setups
An ILP formulation of the Boolean pathway optimization problem fits phosphoproteomic data faster and more efficiently than the previously used genetic algorithm (GA) approach.
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Has reproduction · 84
AI-assisted discovery of an ethnicity-influenced driver of cell transformation in esophageal and gastroesophageal junction adenocarcinomas.
PMID 36134663 · PMC9675486 · JCI insight · 2022 · 8 claims · 8 setups
An AI-guided Boolean network approach (BoNE) models transcriptomic continuum states of normal esophagus, BE, and EAC to derive classifier gene signatures
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Has reproduction · 81
Macrophages on the run: Exercise balances macrophage polarization for improved health.
PMID 39476967 · PMC11585839 · Molecular metabolism · 2024 · 8 claims · 7 setups
Immediate/acute exercise triggers an M1 (pro-inflammatory) macrophage polarization surge.
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Has reproduction · 85
NETISCE: a network-based tool for cell fate reprogramming.
PMID 35725577 · PMC9209484 · NPJ systems biology and applications · 2022 · 8 claims · 4 setups
NETISCE predicts cell fate reprogramming targets in static (GRN/signaling) networks without needing full kinetic parameterization of a dynamical model.