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).

• 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

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  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.

  nine yan dpt scrna-seq trajectory

• An AI-Enabled Single-Cell Transcriptomic Analysis Pipeline for Gene Signature Discovery in Natural Killer Cells Linked to Remission Outcomes in Chronic Myeloid Leukemia.

  PMID 41972591 · PMC13072394 · Biology · 2026 · 8 claims · 7 setups

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  GAFA integrates latent-space representation, pseudotime trajectory modeling, GRN inference, and machine learning-based gene panel discovery into a single coherent pipeline, unlike existing workflows that treat these steps independently.

  peripheral subset immune assigned scrna-seq latent-space automated