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).

• Identifying clinically relevant cell state interactions in the tumor microenvironment of IDH-mutant gliomas using CSI-TME.

  PMID 41807578 · PMC13230996 · Molecular systems biology · 2026 · 7 claims · 8 setups

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  CSI-TME is a computational pipeline that deconvolves bulk tumor RNA-seq into cell-type-specific expression (via CODEFACS), infers transcriptional states per cell type via ICA, and identifies IC pairs from two cell types whose joint activity is associated with survival via Cox regression

  idh-mutant tcga cgga deconvolved scrna-seq bulk independent

• Robust transcriptomic hallmarks targeting intratumor heterogeneity in intrahepatic cholangiocarcinoma.

  PMID 41916296 · PMC13130669 · Cell reports. Medicine · 2026 · 8 claims · 8 setups

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  Immune and stromal heterogeneity, rather than genetic variation, are primary drivers of gene expression ITH in iCCA

  multi-region icca four lihv bulk hallmark single-cell

• 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

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  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

  specified covid-19 scatac scatac-seq bulk

• Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.

  PMID 41758825 · PMC12948085 · PloS one · 2026 · 8 claims · 6 setups

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  AI-based predictive models built on unannotated scRNA-seq data (cell-by-gene expression matrices) can classify melanoma patients as ICI responders vs. non-responders

  cd45+ scrna-seq independent scrna-seq single-gene-level pathway-level

• A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

  PMID 41923359 · PMC13090826 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data

  lncrna hypertrophic gencode lncrna-protein-mirna mlp-based independent ablation