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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Constructing support vector machine ensembles for cancer classification based on proteomic profiling.
PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups
CSVME, built by selecting a subset of base SVMs via SVM-RFE ranking and fusing them with a trained upper-layer SVM, achieves better classification performance than an ensemble of all base SVMs.
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Discovering cancer genes by integrating network and functional properties.
PMID 19765316 · PMC2758898 · BMC medical genomics · 2009 · 8 claims · 6 setups
Cancer genes have distinct PPI network topology (higher connectivity, higher clustering coefficient, shorter path length to known cancer genes) compared to non-cancer genes
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Has reproduction · 69
Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.
PMID 31533612 · PMC6751684 · BMC bioinformatics · 2019 · 8 claims · 6 setups
An ensemble feature selection strategy using consensus of feature relevance across 8 classifier types identifies a 100-miRNA signature from a 1046-feature TCGA dataset
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Has reproduction · 83
Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.
PMID 41688730 · PMC12982753 · Scientific reports · 2026 · 7 claims · 8 setups
170 unique human AFB1 targets were identified by merging ChEMBL, SwissTargetPrediction, and PharmMapper predictions
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Network-assisted protein identification and data interpretation in shotgun proteomics.
PMID 19690572 · PMC2736651 · Molecular systems biology · 2009 · 7 claims · 7 setups
Confidently identified proteins in a sample form tightly connected sub-networks in the protein interaction network, with significantly higher clustering coefficients than random or topology-matched random sub-networks.