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).

• Illuminating cell states by a comprehensive and interpretable single cell foundation model.

  PMID 41839876 · PMC13139411 · Nature communications · 2026 · 6 claims · 6 setups

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  CellVQ, incorporating a Single-Cell Discretization (SCD) module that converts cell embeddings into a discrete 'cell code' via a unified codebook, mitigates data heterogeneity and batch effects while improving interpretability

  pancreas sampled bulk lines scrna-seq type property

• Single-Cell Analysis Reveals Characterization of Infiltrating T Cells in Moderately Differentiated Colorectal Cancer.

  PMID 33584715 · PMC7873865 · Frontiers in immunology · 2020 · 8 claims · 8 setups

  Has reproduction · 45

  Eight distinct T cell populations can be identified from CRC tumor tissue by unsupervised clustering of scRNA-seq data

  tumor-infiltrating peripheral cd4+ tumor single-cell unsupervised pearson

• A unified framework for correcting batch effects and integrating multi-omics data.

  PMID 41786846 · PMC13079841 · Scientific reports · 2026 · 7 claims · 6 setups

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  MoDAmix, a four-stage domain adaptation framework (pre-training, single-omics adversarial adaptation, multi-omics adversarial alignment, semi-supervised class alignment), unifies batch correction across multiple omics layers.

  brain aml same single-cell bulk k-means

• MECP2 mutations rewire human ESC fate and bias cortical lineage commitment.

  PMID 42030940 · PMC13163216 · Stem cell reports · 2026 · 8 claims · 8 setups

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  MECP2 mutations induce an early naïve-like transcriptional drift in human ESCs, marked by upregulation of ZFP42/REX1 and other naïve-enriched markers

  isogenic undifferentiated 3-month bulk bulk single-cell single-nucleus

• EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.

  PMID 41527854 · PMC12796812 · Briefings in bioinformatics · 2026 · 8 claims · 8 setups

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  EpiXFormer achieves high accuracy (mean AUROC ~0.99) predicting binding sites of both TFs and non-sequence-specific DBPs across 199 DBP-cell type pairs

  dbps tfs common ctcf deep comparative feature-ablation