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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Cross-modality representation and multi-sample integration of spatially resolved omics data.
PMID 42114120 · PMC13160428 · Briefings in bioinformatics · 2026 · 7 claims · 4 setups
PRESENT is a contrastive learning-based integrative framework for cross-modality representation of spatial multi-omics data using omics-specific encoders (GAT + BNN) and distribution-aware decoders (ZINB for RNA, ZIP for ATAC)
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High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.
PMID 41887797 · PMC13138010 · Genome research · 2026 · 7 claims · 6 setups
scBOND is a bidirectional dual-channel VAE framework for cross-modality translation between scRNA-seq and scDNAm that outperforms existing baseline methods (scCross, MAPLE) in both translation directions
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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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Partially shared multi-modal embedding learns holistic representation of cell state.
PMID 41741805 · PMC13021527 · Nature computational science · 2026 · 8 claims · 5 setups
APOLLO automatically learns partial information sharing between multiple data modalities using an autoencoder with a partially overlapping latent space trained via latent optimization.
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GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.
PMID 42108634 · PMC13158126 · Briefings in bioinformatics · 2026 · 6 claims · 5 setups
GAMMI consistently outperforms state-of-the-art integration methods (GLUE, Harmony, MIDAS, scMoMaT) in biological conservation and batch correction across five mosaic single-cell multi-omics benchmarks
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Multimodal learning reveals plants' hidden sensory integration logic.
PMID 41714925 · PMC13032346 · BMC genomics · 2026 · 8 claims · 8 setups
CoMM-BIP (Contrastive Multi-Modal learning with Biologically Informed Priors) integrates transcriptomic, metabolomic, and phenomic data using pathway-guided attention, information-theoretic disentanglement, and domain-aware augmentations