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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scSurv: a deep generative model for single-cell survival analysis.
PMID 41429574 · PMC12797213 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups
scSurv combines a Cox proportional hazards model with a deep generative model (VAE) of single-cell transcriptomes to estimate individual cellular contributions to clinical outcomes
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A quantitative proteomics analysis of subcellular proteome localization and changes induced by DNA damage.
PMID 20026476 · PMC2849709 · Molecular & cellular proteomics : MCP · 2010 · 6 claims · 5 setups
A SILAC-based 'spatial proteomics' method can quantitatively measure the relative subcellular distribution of thousands of proteins across cytoplasm, nucleus, and nucleolus.
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Charting spatial ligand-target activity using Renoir.
PMID 42086556 · PMC13144314 · Nature communications · 2026 · 8 claims · 8 setups
Renoir computes a neighborhood activity score for curated ligand-target pairs at each spatial spot/cell by integrating cell type abundance, cell type-specific mRNA abundance, receptor expression, gene entropy, and mutual information between ligand and target genes.
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SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.
PMID 41975540 · PMC13185361 · Genome medicine · 2026 · 8 claims · 8 setups
SpaPheno integrates spatial transcriptomics with clinically annotated bulk RNA-seq to identify spatially resolved biomarkers predictive of patient outcomes including survival, tumor stage, and immunotherapy response
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Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.
PMID 41547856 · PMC12917120 · Nature communications · 2026 · 8 claims · 8 setups
GARDEN identifies and characterizes rare pathogenic cell populations/regions in spatial omics by embedding graph-based dynamic attention into a spatially-aware graph fusion contrastive model
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DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.
PMID 41663685 · PMC12996496 · Communications biology · 2026 · 7 claims · 6 setups
DANST, a deconvolution framework using deep domain adversarial neural networks, achieves superior cell-type deconvolution accuracy compared with existing methods on human and mouse benchmark datasets
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Single-Cell and Spatial Transcriptomics Reveal That TXNIP and BIRC3 Contribute to Human Prostate Tumor Progression.
PMID 41972735 · PMC13072731 · Cells · 2026 · 6 claims · 8 setups
TXNIP and BIRC3 are established as spatially restricted tumor-niche genes associated with metabolic stress and inflammatory survival pathways
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A hormetic transcriptional program coregulates invasion, proliferation and dormancy to define metastatic potential.
PMID 41781391 · PMC13077004 · Nature communications · 2026 · 8 claims · 7 setups
Prrx1 is a master regulator of dissemination that, beyond promoting invasion, represses proliferation (via Ccnd1/2, Cdkn2a/b/c) and activates a dormancy program (Gas6, Mme, Ogn)
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Liquid chromatography-tandem and MALDI imaging mass spectrometry analyses of RCL2/CS100-fixed, paraffin-embedded tissues: proteomics evaluation of an alternate fixative for biomarker discovery.
PMID 19856998 · PMC2924679 · Journal of proteome research · 2009 · 7 claims · 4 setups
RCL2/CS100-fixed tissues yield peptide and protein identifications by nanoRPLC-MS/MS comparable to matched fresh-frozen tissues, with proteome coverage not obviously compromised by fixation.
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Cell neighborhood topology directs rare cell population identification.
PMID 41912521 · PMC13199379 · Nature communications · 2026 · 8 claims · 8 setups
RareQ is a framework that quantifies neighborhood connectivity (Q), a cell-specific measure of kNN-graph cliquishness, to detect rare cell populations from single-cell and spatial omics data