Empirical study using network of semantically related associations in bridging the knowledge gap

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dc.contributor.authorAbedi, Vidaen
dc.contributor.authorYeasin, Mohammeden
dc.contributor.authorZand, Raminen
dc.date.accessioned2014-12-03T04:05:25Zen
dc.date.available2014-12-03T04:05:25Zen
dc.date.issued2014-11-27en
dc.date.updated2014-12-03T04:05:26Zen
dc.description.abstractBackground The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge. Methods In this paper, we highlight some of the findings using a text analytics tool, called ARIANA - Adaptive Robust and Integrative Analysis for finding Novel Associations. Results Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model. Conclusion An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJournal of Translational Medicine. 2014 Nov 27;12(1):324en
dc.identifier.doihttps://doi.org/10.1186/s12967-014-0324-9en
dc.identifier.urihttp://hdl.handle.net/10919/50958en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderVida Abedi et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleEmpirical study using network of semantically related associations in bridging the knowledge gapen
dc.title.serialJournal of Translational Medicineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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