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).

• 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

• Predicting preferential DNA vector insertion sites: implications for functional genomics and gene therapy.

  PMID 18047689 · PMC2106846 · Genome biology · 2007 · 8 claims · 6 setups

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  Vector insertion site preferences differ substantially between viral vectors and transposons, affecting both oncogenic risk in gene therapy and utility for functional genomics

  hela liver gene peripheral retroviral transposon hydrodynamic

• scDenorm: a denormalization tool for integrating single-cell transcriptomics data.

  PMID 41915012 · PMC13142155 · GigaScience · 2026 · 8 claims · 7 setups

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  Inconsistent delta-method normalization across datasets introduces biases (e.g., B-cell separation) that persist even after integration with Harmony, scanorama, or BBKNN.

  10x brain ucsc datasets scrna-seq integration snrna-seq

• Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.

  PMID 41971949 · PMC13068006 · Computational and structural biotechnology journal · 2026 · 8 claims · 6 setups

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  spVIPES decomposes unpaired scRNA-seq datasets with nonmatching features into shared and private latent representations using a Product of Experts framework

  simulated zebrafish kidney peripheral simulated cross-species scrna-seq

• Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution.

  PMID 41980945 · PMC13250166 · Nature communications · 2026 · 8 claims · 8 setups

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  Hi-Compass predicts cell-type-specific Hi-C contact maps using only ATAC-seq as cell-type-specific input, plus DNA sequence and a generalized CTCF binding profile

  gm12878 curated bulk training hi-c ctcf matched

• GRNFormer: accurate gene regulatory network inference using graph transformer.

  PMID 41883144 · PMC13069479 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  GRNFormer is a generalizable graph transformer framework for GRN inference from single-cell or bulk transcriptomics data across species, cell types, and platforms without cell-type annotations or prior regulatory information

  hematopoietic embryonic dendritic hepatocytes scrna-seq chip-seq string