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).

• 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

• Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution.

  PMID 41980945 · PMC13250166 · Nature communications · 2026 · 8 claims · 8 setups

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  Hi-Compass predicts cell-type-specific Hi-C contact maps using only ATAC-seq as cell-type-specific input, plus DNA sequence and a generalized CTCF binding profile

  gm12878 curated bulk training hi-c ctcf matched

• A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

  PMID 41923359 · PMC13090826 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data

  lncrna hypertrophic gencode lncrna-protein-mirna mlp-based independent ablation