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).

• CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.

  PMID 41734268 · PMC12987762 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 5 setups

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  CIRCE re-implements the Cicero co-accessibility algorithm in Python, producing near-identical results while running much faster and using far less memory

  bmmc pbmc foetal cis-regulatory enhancer-promoter scalability

• Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.

  PMID 41821037 · PMC12983708 · Genome biology · 2026 · 8 claims · 8 setups

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  Horizontal integration of scRNA-seq and snRNA-seq improves cell-type identification

  renal kidney pbmc heart scrna-seq snrna-seq snatac-seq

• Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap.

  PMID 41650244 · PMC12890892 · Proceedings of the National Academy of Sciences of the United States of America · 2026 · 8 claims · 7 setups

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  SuperMap learns cross-modal feature mappings directly from unpaired multimodal data without requiring paired training data

  pbmc bone skin embryos scrna-seq scnmt-seq asap-seq

• BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.

  PMID 41632825 · PMC12904586 · PLoS computational biology · 2026 · 8 claims · 5 setups

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  BiCLUM consistently outperforms or matches existing integration methods across multiple RNA+ATAC and RNA+protein datasets in visualization and quantitative benchmarks

  pbmc kidney bmmc bmcite scrna-seq cite-seq

• CellPredX, a computational framework for cross-data type, cross-sample, and cross-protocol cell type annotation through domain adaptation and deep metric learning.

  PMID 41481570 · PMC12758788 · PLoS computational biology · 2026 · 8 claims · 7 setups

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  CellPredX is a unified semi-supervised framework integrating domain adaptation and deep metric learning to align heterogeneous embeddings for cross-modality cell type annotation.

  pbmc fetal pancreas gse149683 matched unmatched cross-protocol