title: A network medicine approach to quantify distance between hereditary disease modules on the interactome.
abstract: We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.
authors.affiliation: Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK.
authors.forename: Horacio
authors.initials: H
authors.lastname: Caniza
authors.affiliation: Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK.
authors.forename: Alfonso E
authors.initials: AE
authors.lastname: Romero
authors.affiliation: Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK.
authors.forename: Alberto
authors.initials: A
authors.lastname: Paccanaro
mesh_terms: D057225:Data Mining / Q000379:methods; D005796:Genes; D030342:Genetic Diseases, Inborn / Q000235:genetics; D006801:Humans; D020407:Internet; D046650:Medical Subject Headings