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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Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues.
PMID 16188928 · PMC4631381 · Bioinformatics (Oxford, England) · 2005 · 7 claims · 4 setups
Genes upregulated in squamous cell lung cancer are highly connected (well-connected) nodes in the protein interactome
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Integrated proteomic and transcriptomic profiling of mouse lung development and Nmyc target genes.
PMID 17486137 · PMC2673710 · Molecular systems biology · 2007 · 8 claims · 7 setups
Global MudPIT-based proteomic profiling across six mouse lung developmental time points (E13.5–P56) identifies thousands of proteins and captures developmental/cell-biological expression patterns.
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Has reproduction · 74
Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer's Disease.
PMID 36518809 · PMC9744611 · Computational intelligence and neuroscience · 2022 · 7 claims · 6 setups
Autoencoder networks based on 266 shared DEGs reveal a comorbidity (positive) relationship between Alzheimer's disease and lung cancer.
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Delineation of a gene network underlying the pulmonary response to oxidative stress in asthma.
PMID 19730131 · PMC3328512 · Journal of investigative medicine : the official publication of the American Federation for Clinical Research · 2009 · 8 claims · 6 setups
Integration of multiple public microarray datasets via a four-way Venn diagram identifies genes commonly expressed in asthma- and cigarette smoke-exposed lung
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Has reproduction · 89
Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.
PMID 37509188 · PMC10377046 · Biomolecules · 2023 · 8 claims · 3 setups
Graph Random Forest (GRF) embeds graph/network information directly into the decision-tree building process by splitting on features in the k-hop neighborhood of a data-driven head-splitting node.