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

• An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation.

  PMID 41922356 · PMC13212934 · Nature communications · 2026 · 8 claims · 7 setups

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  BioSeq2Seq is a transformer-based deep learning framework that predicts genome-wide transcriptional regulatory profiles at 128-bp resolution by jointly using RO-seq data and DNA sequence as tri-modal input

  k562 gm12878 imr-90 ro-seq chip-seq functional

• Architectural and evolutionary features of TE-derived TSSs shape tissue-specific promoter activity in the human genome.

  PMID 41620470 · PMC12963367 · Nature communications · 2026 · 8 claims · 8 setups

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  A three-step RAMPAGE-based pipeline can systematically identify TE-derived transcription start sites (TSSs) genome-wide, distinguishing them from autonomous TE transcription and background noise.

  biosamples same k562 genomic rampage rna-seq h3k4me3