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Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles Cheung, Warren A.; Ouellette, B. F. F.; Wasserman, Wyeth W.

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.

Item Metadata

Title
Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles
Creator
Cheung, Warren A.; Ouellette, B. F. F.; Wasserman, Wyeth W.
Contributor
Child and Family Research Institute
Publisher
BioMed Central
Date Issued
2012-09-28
Description
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.
Genre
Article
Type
Text
Language
eng
Date Available
2016-02-09
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0224015
URI
http://hdl.handle.net/2429/56930
Affiliation
Science, Faculty of; Medical Genetics, Department of; Medicine, Faculty of; Molecular Medicine and Therapeutics, Centre for; Non UBC
Citation
Genome Medicine. 2012 Sep 28;4(9):75
Publisher DOI
10.1186/gm376
Peer Review Status
Reviewed
Scholarly Level
Faculty
Copyright Holder
Cheung et al.; licensee BioMed Central Ltd.
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Rights

Attribution 4.0 International (CC BY 4.0)