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    Systematic identification of latent disease-gene associations from PubMed articles

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    Author
    Zhang, Y.
    Shen, F.
    Mojarad, M.R.
    Date
    2018
    Journal
    PLoS ONE
    Publisher
    Public Library of Science
    Type
    Article
    
    Metadata
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    See at
    https://dx.doi.org/10.1371/journal.pone.0191568
    Abstract
    Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. Copyright 2018 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Keyword
    disease-gene association
    latent Dirichlet allocation (LDA) modeling
    Disease--genetics
    Genetic Association Studies
    PubMed
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041044044&doi=10.1371%2fjournal.pone.0191568&partnerID=40&md5=aa6e9171f858f0d1a6a238bf8d3e5550; http://hdl.handle.net/10713/9395
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0191568
    Scopus Count
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    UMB Open Access Articles 2018

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