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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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
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
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Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.
PMID 42100854 · PMC13229940 · Nucleic acids research · 2026 · 8 claims · 6 setups
SCEG-HiC, a weighted graphical lasso (wglasso) method, predicts enhancer-gene links from single-cell multi-omics data by integrating bulk average Hi-C as a prior penalty matrix
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Single-cell multiome and enhancer connectome of human retinal pigment epithelium and choroid nominate causal variants in macular degeneration.
PMID 41528844 · PMC12971065 · Cell reports · 2026 · 8 claims · 8 setups
Generated a single-cell gene expression and chromatin accessibility (multiome) atlas of human RPE and choroid from control and AMD eyes
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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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Boolean logic links chromatin accessibility states to gene expression variability across cell types.
PMID 41909952 · PMC13148175 · Nucleic acids research · 2026 · 7 claims · 4 setups
ocrRBBR infers interpretable Boolean rules from combinations of accessible OCRs that explain gene expression variability across cell types
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EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.
PMID 41832145 · PMC13133354 · Nature communications · 2026 · 8 claims · 7 setups
EPInformer outperforms existing gene expression prediction models (Xpresso, CREaTor, Seq-GraphReg, Enformer, Borzoi) in rigorous 12-fold cross-chromosome validation for both RNA-seq and CAGE expression prediction