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).
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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
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.
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Deep-learning prediction of gene expression from personal genomes.
PMID 41495833 · PMC12869966 · Genome biology · 2026 · 8 claims · 8 setups
Fine-tuning Enformer on paired personal WGS and RNA-seq data (Variformer) corrects Enformer's failure to predict inter-individual gene expression differences across held-out people.
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Modeling nascent transcription from chromatin landscape and structure with CLASTER.
PMID 41691282 · PMC13011747 · Genome biology · 2026 · 7 claims · 8 setups
CLASTER, a deep neural network combining chromatin landscape tracks and 3D contact maps, accurately predicts kilobasepair-resolution nascent RNA (EU-seq) profiles in a DNA-sequence-agnostic manner
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ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.
PMID 41592570 · PMC13069865 · Cell genomics · 2026 · 8 claims · 7 setups
ChromBERT is pre-trained via masked reconstruction on the Cistrome-Human-6K dataset (6,391 cistromes, 991 transcription regulators) to learn genome-wide interaction syntax of transcription regulators
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Has reproduction
Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets.
PMID 35672799 · PMC9175345 · Genome biology · 2022 · 8 claims · 7 setups
CD4+ T cells and B cells independently and significantly contribute to MS GWAS heritability enrichment, distinct from a merely shared immune regulatory landscape.
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Variant-resolved prediction of context-specific isoform variation with a graph-based attention model.
PMID 41547351 · PMC13069856 · Cell genomics · 2026 · 8 claims · 8 setups
Otari, an attention-based graph neural network trained on long-read transcriptomes across 30 tissues/brain regions, predicts tissue-specific differential isoform abundance