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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iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.
PMID 41775921 · PMC13066597 · Communications biology · 2026 · 8 claims · 5 setups
iAODE combines a ZINB-likelihood VAE, a latent Neural ODE, low-weight KL regularization, and an interpretable reconstruction (irecon) bottleneck to learn generative, temporally continuous latent spaces for scATAC-seq.
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OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.
PMID 41335419 · PMC12766913 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups
OTMODE, using an unbalanced Sinkhorn algorithm and Wald test, improves differential feature identification in single-cell multi-omics data
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scCNMF: an integrated analysis model for paired single-cell RNA sequencing and assay for transposase-accessible chromatin sequencing data leveraging cell similarity and cis-regulatory potential.
PMID 41800139 · PMC12962131 · PeerJ · 2026 · 7 claims · 2 setups
scCNMF is an NMF-based model for vertical integration of paired scRNA-seq and scATAC-seq data that jointly incorporates a cell similarity matrix and a cis-regulatory potential (CRP) matrix
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A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.
PMID 41980989 · PMC13253844 · Nature communications · 2026 · 8 claims · 7 setups
scDiffusion-X is a multi-modal latent denoising diffusion probabilistic model for single-cell multi-omics data generation, translation, and interpretation.
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Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic integration.
PMID 42054434 · PMC13148828 · PLoS computational biology · 2026 · 8 claims · 5 setups
SEBULA models the singlet background directly from observed HCLC (high-coverage locus count) statistics using fragment-level snATAC-seq information, avoiding reliance on synthetic/artificial doublets.