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).

• Human neuronal differentiation under Aβ exposure: a single-cell transcriptomic and epigenomic dataset.

  PMID 41807428 · PMC13103403 · Scientific data · 2026 · 8 claims · 4 setups

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  A paired scRNA-seq and scATAC-seq dataset was generated from NPCs differentiated over Days 0, 7, 13, and 20 under baseline and Aβ 1-42 exposure conditions

  hipsc-derived hippocampal scrna-seq scatac-seq flow

• Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings.

  PMID 41555918 · PMC12812404 · Frontiers in genetics · 2025 · 7 claims · 5 setups

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  LiVAE, a dual-pathway VAE with Lorentzian geometric regularization between a primary Euclidean pathway and an information-bottleneck pathway, balances local fidelity with global topology coherence in single-cell embeddings

  single-cell same dapp1 benchmarking umap component-wise

• OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.

  PMID 41335419 · PMC12766913 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  OTMODE, using an unbalanced Sinkhorn algorithm and Wald test, improves differential feature identification in single-cell multi-omics data

  simulated classical degs naive simulated bulk real

• Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.

  PMID 42026624 · PMC13238118 · Genome biology · 2026 · 8 claims · 6 setups

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  SEAGALL combines a geometry-regularised autoencoder (GRAE) to embed cells and build a cell-cell graph with a graph attention network (GAT) classifier and GNNExplainer-based XAI to identify features driving cell type/phenotype.

  count same scchip-seq ten count latent classification

• Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic integration.

  PMID 42054434 · PMC13148828 · PLoS computational biology · 2026 · 8 claims · 5 setups

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  SEBULA models the singlet background directly from observed HCLC (high-coverage locus count) statistics using fragment-level snATAC-seq information, avoiding reliance on synthetic/artificial doublets.

  dogma-seq dogma-seq-derived seven snatac-seq q-q density