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).

• Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists.

  PMID 16351744 · PMC1866390 · BMC bioinformatics · 2005 · 5 claims · 3 setups

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  OMIM Clinical Synopsis free-text phenotype and location names can be normalized and hierarchically structured into a controlled vocabulary suitable for computational analysis

  omim gene text statistical gene

• POCUS: mining genomic sequence annotation to predict disease genes.

  PMID 14611661 · PMC329128 · Genome biology · 2003 · 8 claims · 6 setups

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  Genes predisposing to the same disease tend to share functional annotation IDs (GO/InterPro) more than expected by chance

  omim genome disease comparative pocus expression-id

• Disease-aging network reveals significant roles of aging genes in connecting genetic diseases.

  PMID 19779549 · PMC2739292 · PLoS computational biology · 2009 · 8 claims · 8 setups

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  Human disease genes are much closer to aging genes in the PPI network than expected by chance

  protein-protein randomized dan aging network degree-conserved closeness

• Phenotypic categorization of genetic skin diseases reveals new relations between phenotypes, genes and pathways.

  PMID 19744994 · PMC2773259 · Bioinformatics (Oxford, England) · 2009 · 8 claims · 5 setups

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  560 genetic skin diseases can be decomposed into 71 elementary phenotypic features (42 dermatologic, 29 systemic) that combine to represent each disease as a point in a multidimensional phenotype space

  genodermatoses phenotype disease gene-phenotype manual correlation principal

• 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

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  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

  seed ad-related retrieved combined protein-protein network-based pubmed

• Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

  PMID 15767279 · PMC1065256 · Nucleic acids research · 2005 · 7 claims · 6 setups

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  Integrating eVOC anatomical ontology-based text-mining of PubMed abstracts with data-mining of gene expression annotation successfully selects and prioritizes candidate disease genes

  biomedical refseq training independent text-mining data-mining candidate

• The role of positive selection in determining the molecular cause of species differences in disease.

  PMID 18837980 · PMC2576240 · BMC evolutionary biology · 2008 · 8 claims · 6 setups

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  Genes predicted to be under positive selection during human evolution are implicated in diseases (epithelial cancers, schizophrenia, autoimmune diseases, Alzheimer's disease) that differ in prevalence and symptomatology between humans and other mammals

  orthologous omim meta-database panther comparative disease protein-protein

• Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies.

  PMID 18263617 · PMC2275105 · Nucleic acids research · 2008 · 8 claims · 7 setups

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  Genes correlated with a common complex trait are more likely to share GO functional annotations than genes not correlated with that trait

  genes candidate simulated alzheimer's genome-wide term null-hypothesis

• Using random walks to identify cancer-associated modules in expression data.

  PMID 24128261 · PMC4015830 · BioData mining · 2013 · 8 claims · 8 setups

  Has reproduction

  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.

  hepatocellular breast sporadic geo gene differential interaction