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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PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.
PMID 42044093 · PMC13119913 · PLoS computational biology · 2026 · 8 claims · 3 setups
PICDGI is a Bayesian framework that predicts driver-like regulatory genes by integrating dynamic gene-gene interaction modeling with scRNA-seq data, without using DNA mutation calls
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scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.
PMID 41981652 · PMC13188691 · BMC biology · 2026 · 7 claims · 3 setups
scDEBGCL is a deep embedding method for scRNA-seq data based on bipartite graph contrastive learning, integrating contrastive learning, graph reconstruction, and ZINB-based data reconstruction losses.
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Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.
PMID 41952215 · PMC13174004 · Genome biology · 2026 · 8 claims · 5 setups
No prior systematic, quantitative benchmark exists for CCI inference methods specifically developed for spatial transcriptomics across multiple platforms
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Construction of a SEMA3 family-based model to predict prognosis and molecular subtypes in pancreatic ductal adenocarcinoma.
PMID 41792569 · PMC13079247 · Discover oncology · 2026 · 8 claims · 8 setups
SEMA3 family expression defines two molecular subtypes (A and B) of PDAC with significantly different overall survival