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).

• scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.

  PMID 41746287 · PMC13012890 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups

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  scDBic improves cell clustering by combining deep autoencoder-based cell clustering, gene clustering, and reverse-strategy identification of key gene clusters

  e-mtab-3321 e-mtab-2600 gse45719 gse52529 biclustering ablation

• S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.

  PMID 41556263 · PMC13042551 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  S3RL is a separable representation learning framework that denoises sparse spatial transcriptomic data and enhances biologically relevant signals by integrating gene expression, spatial coordinates, and histological image features.

  dorsolateral dlpfc nanostring spatial cell-type multi-platform

• SMART: spatial multi-omic aggregation using graph neural networks and metric learning.

  PMID 41896208 · PMC13031631 · Nature communications · 2026 · 8 claims · 5 setups

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  SMART accurately identifies spatial regions of anatomical structures and is compatible with spatial datasets of any type and number of omics layers

  simulated lymph brain spleen simulated spatial multi-section