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

• 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

• DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.

  PMID 34261540 · PMC8281595 · Genome medicine · 2021 · 8 claims · 7 setups

  Has reproduction · 84

  DeepProg is a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data

  cancer breast hepatocarcinoma tcga multi-omics gene rna-seq

• Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity.

  PMID 41950003 · PMC13244359 · Cell reports · 2026 · 8 claims · 7 setups

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  Multi-omics factor analysis (MOFA) integrating seven omics views from 1,358 ROS/MAP participants identifies cross-omics biological factors relating to AD phenotypes.

  dorsolateral brain postmortem cortex h3k9ac bulk proteomics

• 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