Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-09

AUTHORS

Warren A Cheung, BF Francis Ouellette, Wyeth W Wasserman

ABSTRACT

BACKGROUND: MEDLINE(®)/PubMed(®) currently indexes over 18 million biomedical articles, providing unprecedented opportunities and challenges for text analysis. Using Medical Subject Heading Over-representation Profiles (MeSHOPs), an entity of interest can be robustly summarized, quantitatively identifying associated biomedical terms and predicting novel indirect associations. METHODS: A procedure is introduced for quantitative comparison of MeSHOPs derived from a group of MEDLINE(®) articles for a biomedical topic (for example, articles for a specific gene or disease). Similarity scores are computed to compare MeSHOPs of genes and diseases. RESULTS: Similarity scores successfully infer novel associations between diseases and genes. The number of papers addressing a gene or disease has a strong influence on predicted associations, revealing an important bias for gene-disease relationship prediction. Predictions derived from comparisons of MeSHOPs achieves a mean 8% AUC improvement in the identification of gene-disease relationships compared to gene-independent baseline properties. CONCLUSIONS: MeSHOP comparisons are demonstrated to provide predictive capacity for novel relationships between genes and human diseases. We demonstrate the impact of literature bias on the performance of gene-disease prediction methods. MeSHOPs provide a rich source of annotation to facilitate relationship discovery in biomedical informatics. More... »

PAGES

75

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/gm376

    DOI

    http://dx.doi.org/10.1186/gm376

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045606561

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23021552


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    RDF/XML is a standard XML format for linked data.

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