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

  Full-text index only

  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

• Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

  PMID 32825264 · PMC7563725 · Biomolecules · 2020 · 6 claims · 7 setups

  Has reproduction · 80

  Combining WGCNA hub genes and VAE 10-dimensional representation as features for an SVM classifier achieves high accuracy in predicting CRC

  colorectal same combined meturquoise microarray differential batch

• 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