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).

• scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation.

  PMID 41632596 · PMC12866930 · Briefings in bioinformatics · 2026 · 8 claims · 6 setups

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  scGACL, a GAN integrated with multi-scale contrastive learning, is proposed to overcome the over-smoothing problem in scRNA-seq imputation

  simulated real-world scrna-seq scrna-seq clustering gene