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 · 80
Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.
PMID 32825264 · PMC7563725 · Biomolecules · 2020 · 6 claims · 7 setups
Combining WGCNA-derived hub genes with a VAE-derived 10-dimensional representation as features for an SVM classifier achieves high accuracy (0.9692) and AUC (0.9981) for colorectal cancer prediction.
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Has reproduction · 80
Comprehensive analysis of transcriptomics and radiomics revealed the potential of TEDC2 as a diagnostic marker for lung adenocarcinoma.
PMID 39553728 · PMC11569783 · PeerJ · 2024 · 8 claims · 8 setups
WGCNA identified 214 key genes in the blue module most correlated with LUAD
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Has reproduction · 49
Integrative transcriptomics and single-cell transcriptomics analyses reveal potential biomarkers and mechanisms of action in papillary thyroid carcinoma.
PMID 40520228 · PMC12162626 · Frontiers in genetics · 2025 · 8 claims · 8 setups
ENTPD1, SERPINA1, and TACSTD2 are potential transcriptomic biomarkers for PTC
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Has reproduction · 59
Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors.
PMID 37887568 · PMC10605255 · Current oncology (Toronto, Ont.) · 2023 · 8 claims · 7 setups
Multi-gene random forest models classify primary tumor vs. liver metastasis samples with 100% accuracy in training/test cohorts and >90% accuracy in an independent validation cohort
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Full-text index only
Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data.
PMID 19733729 · PMC2748225 · Analytica chimica acta · 2009 · 8 claims · 4 setups
ACA-based wavelet coefficient feature selection can achieve up to 100% classification accuracy on training, validating, and independent testing sets using only 5 selected features.