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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Speeding disease gene discovery by sequence based candidate prioritization.
PMID 15766383 · PMC1274252 · BMC bioinformatics · 2005 · 7 claims · 8 setups
Disease genes (OMIM) differ significantly from non-disease genes in sequence-based features including gene/cDNA/protein size, exon number, homolog conservation, secretion signal, 3' UTR length, CpG islands, and distance to nearest gene.
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POCUS: mining genomic sequence annotation to predict disease genes.
PMID 14611661 · PMC329128 · Genome biology · 2003 · 8 claims · 6 setups
Genes predisposing to the same disease tend to share functional annotation IDs (GO/InterPro) more than expected by chance
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Genome bioinformatic analysis of nonsynonymous SNPs.
PMID 17708757 · PMC1978506 · BMC bioinformatics · 2007 · 8 claims · 8 setups
Structure- and sequence-based prediction tools can generally distinguish disease-causing mutations from neutral ones
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Mapping proteins to disease terminologies: from UniProt to MeSH.
PMID 18460185 · PMC2367626 · BMC bioinformatics · 2008 · 8 claims · 7 setups
Developed a three-step procedure (disease name extraction, exact matching, partial/similarity-based matching) to map UniProtKB/Swiss-Prot disease names to MeSH terms
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Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts.
PMID 19649302 · PMC2709445 · PLoS computational biology · 2009 · 7 claims · 4 setups
A computational framework can build disease-specific drug-protein connectivity maps by integrating protein interaction networks and PubMed literature mining, without gene expression profiles from drug perturbation experiments
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Gene-centric characteristics of genome-wide association studies.
PMID 18060058 · PMC2092383 · PloS one · 2007 · 8 claims · 5 setups
High-density SNP chips using either direct or indirect selection approaches provide very high coverage in genic regions and capture most known common disease variants under the HapMap framework.
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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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Update of the G2D tool for prioritization of gene candidates to inherited diseases.
PMID 17478516 · PMC1933178 · Nucleic acids research · 2007 · 8 claims · 4 setups
G2D is a web server that prioritizes candidate genes for inherited diseases using three distinct algorithms based on different input information.
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Has reproduction
Using random walks to identify cancer-associated modules in expression data.
PMID 24128261 · PMC4015830 · BioData mining · 2013 · 8 claims · 8 setups
Walktrap-GM, a random-walk community detection algorithm adapted with stopping criteria (maximum modularity, maximum size, maximum module score), identifies modules significantly enriched with cancer genes in expression-weighted interaction networks.
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Correlation of microsynteny conservation and disease gene distribution in mammalian genomes.
PMID 19909546 · PMC2779822 · BMC genomics · 2009 · 7 claims · 8 setups
Density of mouse orthologs of human disease genes correlates with regions of conserved microsynteny in the mouse genome
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Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network.
PMID 19728866 · PMC2768980 · Genome biology · 2009 · 6 claims · 6 setups
Integrating 16 genomic features (32 sub-features) via a naïve Bayes classifier produces a genome-scale FLN of 21,657 human genes and 22,388,609 weighted links that outperforms any individual data source for inferring functional linkages.
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Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.
PMID 16757574 · PMC1475747 · Nucleic acids research · 2006 · 6 claims · 8 setups
Applying seven independent computational disease-gene prioritization methods in concert to 9556 positional candidate genes identifies a prioritized set of likely T2D and obesity candidate genes
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MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease.
PMID 19758471 · PMC2745589 · BMC bioinformatics · 2009 · 8 claims · 5 setups
MtSNPscore, a weighted scoring pipeline combining literature evidence, in silico predictions, and case/control frequency, can prioritize likely pathogenic mtDNA variations