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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Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.
PMID 41869863 · PMC13170260 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups
Among ten/eleven evaluated single-cell FMs, scGPT, Geneformer, and CellFM are the top models considering both performance and user accessibility
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A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.
PMID 41923359 · PMC13090826 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups
LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data