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).

• 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

• 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

• From forest to farm: the impact of a broad spectrum of lifestyles on the porcine gut microbiota.

  PMID 41810129 · PMC12969317 · Current research in microbial sciences · 2026 · 8 claims · 6 setups

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  Dietary variables had the largest effect on microbiota composition among lifestyle factors tested

  faecal 16s microbiota 16s volatile faecal

• Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants.

  PMID 41647537 · PMC12869734 · Fundamental research · 2026 · 6 claims · 3 setups

  Has reproduction · 68

  scCluster, an end-to-end deep clustering model integrating gene expression and expressed variant (eSNP) features, stratifies cell subpopulations in cancer scRNA-seq data.

  raw real-world expressed gene deep

• Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.

  PMID 41820396 · PMC13121706 · Nature communications · 2026 · 8 claims · 7 setups

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  TRIM, a conditional multi-modal variational autoencoder integrating paired scRNAseq and scTCRseq data, predicts T cell clonality and transcriptional states at unmeasured tissue sites/timepoints.

  hnscc colorectal pan-cancer tcr paired umap convolutional