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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Has reproduction · 58
Comprehensive analysis of peroxisome proliferator-activated receptors to predict the drug resistance, immune microenvironment, and prognosis in stomach adenocarcinomas.
PMID 38529307 · PMC10962337 · PeerJ · 2024 · 8 claims · 8 setups
PPARA, PPARD and PPARG are more abnormally expressed in STAD samples and cell lines compared to most of 32 cancer types in TCGA
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Has reproduction · 51
Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.
PMID 32542016 · PMC7295234 · PloS one · 2020 · 7 claims · 8 setups
Five tyrosine catabolic enzymes (TAT, HPD, HGD, GSTZ1, FAH) are downregulated in HCC compared to normal liver at mRNA and/or protein level
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Has reproduction · 55
Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.
PMID 37691920 · PMC10485775 · Frontiers in immunology · 2023 · 8 claims · 8 setups
352 DEGs (198 up-regulated, 154 down-regulated) were identified between RPL and control endometrial samples
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Has reproduction · 59
Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer.
PMID 41369820 · PMC12799884 · Discover oncology · 2025 · 8 claims · 8 setups
Key genes AR, AKT1, UBC, CDH1, SMAD3, ROR1, and ROR2 are associated with chemotherapy resistance and poor prognosis in metastatic breast cancer
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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