Show simple item record Wild, David J. Qiu, Judy He, Bing Dong, Xiao Tang, Jie Ding, Ying Wang, Huijun 2012-04-09T17:38:10Z 2012-04-09T17:38:10Z 2011-03-23
dc.identifier.citation Wang H, Ding Y, Tang J, Dong X, He B, et al. (2011) Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA. PLoS ONE 6(3): e17243. doi:10.1371/journal.pone.0017243 en
dc.identifier.uri http://10.1371/journal.pone.0017243 en
dc.description.abstract The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing. en
dc.language.iso en_US en
dc.publisher PLoS en
dc.rights Copyright 2011 Wang 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
dc.title Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA en
dc.type Article en

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