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).

• 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

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  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.

  count same scchip-seq ten count latent classification

• 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

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  LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data

  lncrna hypertrophic gencode lncrna-protein-mirna mlp-based independent ablation