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dc.contributor.authorZhang, Y.
dc.contributor.authorShen, F.
dc.contributor.authorMojarad, M.R.
dc.date.accessioned2019-06-05T18:28:13Z
dc.date.available2019-06-05T18:28:13Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041044044&doi=10.1371%2fjournal.pone.0191568&partnerID=40&md5=aa6e9171f858f0d1a6a238bf8d3e5550
dc.identifier.urihttp://hdl.handle.net/10713/9395
dc.description.abstractRecent 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.en_US
dc.description.urihttps://dx.doi.org/10.1371/journal.pone.0191568en_US
dc.language.isoen-USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS ONE
dc.subjectdisease-gene associationen_US
dc.subjectlatent Dirichlet allocation (LDA) modelingen_US
dc.subject.meshDisease--geneticsen_US
dc.subject.meshGenetic Association Studiesen_US
dc.subject.meshPubMeden_US
dc.titleSystematic identification of latent disease-gene associations from PubMed articlesen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0191568
dc.identifier.pmid29373609


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