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 · 32
Developing prognostic gene panel of survival time in lung adenocarcinoma patients using machine learning.
PMID 35117753 · PMC8799101 · Translational cancer research · 2020 · 7 claims · 4 setups
A panel of 22 genetic features with Naïve Bayes can predict whether lung adenocarcinoma patient survival time is >3 years (accuracy=75%, AUC=0.81).
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Has reproduction · 88
Constructing an APOBEC-related gene signature with predictive value in the overall survival and therapeutic sensitivity in lung adenocarcinoma.
PMID 37954334 · PMC10637964 · Heliyon · 2023 · 8 claims · 8 setups
APOBEC family genes are aberrantly expressed across cancers, with APOBEC3B the most consistently upregulated, and APOBEC3B/APOBEC3A are markedly altered in LUAD
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Has reproduction · 90
Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma.
PMID 37948380 · PMC10637681 · PloS one · 2023 · 6 claims · 7 setups
Three stem cell-related genes (ORC1L, KIF20A, DLGAP5) are differentially expressed in LUAD and correlate with altered immune infiltration and reduced patient survival.
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Has reproduction · 77
SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images.
PMID 38539106 · PMC10967213 · BMC bioinformatics · 2024 · 7 claims · 6 setups
SurvConvMixer reformats KEGG Pathways-in-Cancer gene expression values into pathway-level 2D gene expression images and applies a ConvMixer-based model for overall survival prediction
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Has reproduction · 83
Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.
PMID 39856156 · PMC11760357 · Scientific reports · 2025 · 7 claims · 8 setups
Two lactylation cancer subtypes (CS1, CS2) can be identified in LUAD by multiomics ensemble clustering, with CS1 linked to better overall survival than CS2
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Has reproduction · 59
Identifying Molecular Subtypes and 6-Gene Prognostic Signature Based on Hypoxia for Optimizing Targeted Therapies in Non-Small Cell Lung Cancer.
PMID 35509605 · PMC9058021 · International journal of general medicine · 2022 · 8 claims · 8 setups
NSCLC samples can be classified into two molecular subtypes (C1 and C2) based on hypoxia-related gene expression via consensus clustering
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Has reproduction · 71
Interpretable artificial intelligence based on immunoregulation-related genes predicts prognosis and immunotherapy response in lung adenocarcinoma.
PMID 41048340 · PMC12491262 · Frontiers in bioinformatics · 2025 · 8 claims · 8 setups
LUAD samples cluster into IRG-high and IRG-low groups, with the IRG-high group showing significantly better survival and greater immune cell infiltration.
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Has reproduction · 67
Heterogeneity and Differentiation Trajectories of Infiltrating CD8+ T Cells in Lung Adenocarcinoma.
PMID 36358600 · PMC9658355 · Cancers · 2022 · 7 claims · 8 setups
Infiltrating CD8+ T cells in LUAD can be divided into ten transcriptionally distinct subsets: eight cytotoxic (CTL) subsets, one naive-like (NTL) subset, and one exhausted (ETL) subset.
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Has reproduction · 59
Downregulation of Splicing Factor PTBP1 Curtails FBXO5 Expression to Promote Cellular Senescence in Lung Adenocarcinoma.
PMID 39057099 · PMC11276454 · Current issues in molecular biology · 2024 · 8 claims · 8 setups
PTBP1 is overexpressed in LUAD and associated with poor prognosis/tumor growth, indicating an oncogenic role.
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Has reproduction · 62
scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.
PMID 40501071 · PMC12159290 · Briefings in bioinformatics · 2025 · 7 claims · 8 setups
scATD enables high-throughput single-cell drug sensitivity prediction for new patients without model parameter retraining via bidirectional style transfer (Bi-AdaIN).