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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Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response.
PMID 38538696 · PMC10973470 · NPJ precision oncology · 2024 · 8 claims · 8 setups
RCD score, defined as the sum of ssGSEA scores of 18 RCD signatures, quantifies overall RCD signaling in a sample
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Has reproduction · 94
iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration.
PMID 37023414 · PMC10236149 · Cancer immunology research · 2023 · 8 claims · 8 setups
Malignant cells cluster by patient in scRNA-seq data while immune and stromal cells cluster by cell type, making malignant cells uniquely suited to carry patient-level inflamed/cold signal
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Multimodal profiling of pancreatic cancer reveals a TIMP-1-dominated secretory profile determining pro-tumor immunoinstruction in human cancers.
PMID 41564862 · PMC12866174 · Cell reports. Medicine · 2026 · 8 claims · 8 setups
A 19-factor cancer-immunoinstructive secretory signature (CISS) is present across multiple human cancers and correlates with pro-tumorigenic tumor microenvironments and poor patient survival.
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Colorectal microenvironment determines the prognosis of colorectal cancer.
PMID 41495419 · PMC12868731 · Experimental & molecular medicine · 2026 · 8 claims · 7 setups
The colorectal microenvironment (classified via NBT gene expression as tumor-supportive vs healthy) can serve as a prognostic biomarker predicting cancer invasiveness and recurrence
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Pan-Cancer Single-Cell RNA Sequencing Analysis Refines Multi-Origin Monocyte and Macrophage Lineages.
PMID 41231218 · PMC12865363 · Cancer immunology research · 2026 · 6 claims · 8 setups
TAMs arise from two distinct origins: C1QC+ TAMs likely derive from resident tissue macrophages, while SPP1+ TAMs and ISG15+ TAMs likely originate from circulating monocytes.
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DLX2 marks an immunosuppressive dendritic-cell program that reshapes cytotoxic immunity and marks a tolerogenic microenvironment in lung adenocarcinoma.
PMID 41761000 · PMC13043845 · Discover oncology · 2026 · 8 claims · 8 setups
Neuroactive ligand–receptor signaling is among the most significantly upregulated pathways in LUAD relative to normal lung tissue.
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Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.
PMID 41820396 · PMC13121706 · Nature communications · 2026 · 8 claims · 7 setups
TRIM, a conditional multi-modal variational autoencoder integrating paired scRNAseq and scTCRseq data, predicts T cell clonality and transcriptional states at unmeasured tissue sites/timepoints.
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Advancing Prognosis Prediction and Immunotherapy Efficacy in Lung Adenocarcinoma Through Machine Learning: Novel Insights From Anoikis Regulator Patterns in Single-Cell Multiomics.
PMID 41488744 · PMC12764181 · International journal of genomics · 2026 · 8 claims · 8 setups
Epithelial and endothelial cells show the highest anoikis-enriched scores among LUAD TME cell types, with AT2-like Epi being the most anoikis-related epithelial subpopulation.
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Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.
PMID 41566530 · PMC12906006 · Genome biology · 2026 · 7 claims · 6 setups
Pseudobulk and real bulk RNA-seq deconvolution performance differ significantly, and method ranking consistency is lower between pseudobulk and real bulk than within either data type alone
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Has reproduction · 84
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.
PMID 34261540 · PMC8281595 · Genome medicine · 2021 · 8 claims · 7 setups
DeepProg is a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data