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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Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.
PMID 42026624 · PMC13238118 · Genome biology · 2026 · 8 claims · 6 setups
SEAGALL combines a geometry-regularised autoencoder (GRAE) to embed cells and build a cell-cell graph with a graph attention network (GAT) classifier and GNNExplainer-based XAI to identify features driving cell type/phenotype.
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Vertebrate gene finding from multiple-species alignments using a two-level strategy.
PMID 16925840 · PMC1810555 · Genome biology · 2006 · 8 claims · 5 setups
DOGFISH cleanly separates a multi-species alignment classifier (RVM cascade) from an HMM-based structure predictor, avoiding tight coupling of alignment complexity with HMM formalism
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Speeding disease gene discovery by sequence based candidate prioritization.
PMID 15766383 · PMC1274252 · BMC bioinformatics · 2005 · 7 claims · 8 setups
Disease genes (OMIM) differ significantly from non-disease genes in sequence-based features including gene/cDNA/protein size, exon number, homolog conservation, secretion signal, 3' UTR length, CpG islands, and distance to nearest gene.
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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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Discovering cancer genes by integrating network and functional properties.
PMID 19765316 · PMC2758898 · BMC medical genomics · 2009 · 8 claims · 6 setups
Cancer genes have distinct PPI network topology (higher connectivity, higher clustering coefficient, shorter path length to known cancer genes) compared to non-cancer genes