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

• Harvesting more reads from single-cell combinatorial barcoding data with scarecrow.

  PMID 41967853 · PMC13125751 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 3 setups

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  scarecrow screens a subsample of reads to generate position-specific barcode profiles, then flexibly identifies barcode sequences in reads while accounting for positional jitter

  pbmc scrna-seq single-cell

• ProtoCloud: A prototypical self-explaining model for single-cell analysis.

  PMID 41997134 · PMC13261663 · Cell genomics · 2026 · 8 claims · 8 setups

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  ProtoCloud matches or outperforms existing annotation methods across 11 large-scale datasets, particularly for rare cell types

  diverse pbmc10k pbmc30k tsca scrna-seq latent gene