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).

• Mapping proteins to disease terminologies: from UniProt to MeSH.

  PMID 18460185 · PMC2367626 · BMC bioinformatics · 2008 · 8 claims · 7 setups

  Full-text index only

  Developed a three-step procedure (disease name extraction, exact matching, partial/similarity-based matching) to map UniProtKB/Swiss-Prot disease names to MeSH terms

  randomly swiss-prot uniprotkb mesh manual exact partial

• CGMIM: automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes.

  PMID 15796777 · PMC1274267 · BMC bioinformatics · 2005 · 8 claims · 2 setups

  Full-text index only

  CGMIM is a Perl program that text-mines OMIM entries to identify cancer-gene associations and genetically-related cancer type pairs.

  omim automated manual

• 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

  Full-text index only

  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

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

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

  Full-text index only

  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

• 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

  Full-text index only

  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

  pubmed genes same randomly geneseeker evoc disease