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).

• CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.

  PMID 41555433 · PMC12903630 · Genome biology · 2026 · 8 claims · 8 setups

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  CLAMP, a chromatin-accessibility language model, predicts protein-mediated chromatin loops across 10 species, 18 proteins, and 24 cell types with superior performance versus existing methods.

  species gm12878 types chromatin cross-protein cross-cell-type

• 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

• Ab initio identification of human microRNAs based on structure motifs.

  PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups

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  MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence

  genome mirna hairpins chromosome secondary linear five-fold

• CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.

  PMID 41498638 · PMC12948241 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  CellPolaris is a unified computational framework performing TF-centered GRN construction, master TF identification, and TF perturbation simulation

  bulk previously round paired transfer-learning cross-species