Connecting the Dots between PubMed Abstracts

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dc.contributor.authorHossain, M. Shahriaren
dc.contributor.authorGresock, Josephen
dc.contributor.authorEdmonds, Yvette M.en
dc.contributor.authorHelm, Richard F.en
dc.contributor.authorPotts, Malcolmen
dc.contributor.authorRamakrishnan, Narenen
dc.contributor.departmentBiochemistryen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-10-31T12:46:12Zen
dc.date.available2018-10-31T12:46:12Zen
dc.date.issued2012-01-03en
dc.description.abstractBackground There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications. Methodology We present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for “connecting the dots” across the literature. We describe a storytelling algorithm that, given a start and end publication, typically with little or no overlap in content, identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. The quality of discovered stories is measured using local criteria such as the size of supporting neighborhoods for each link and the strength of individual links connecting publications, as well as global metrics of dispersion. To ensure that the story stays coherent as it meanders from one publication to another, we demonstrate the design of novel coherence and overlap filters for use as post-processing steps. Conclusions We demonstrate the application of our storytelling algorithm to three case studies: i) a many-one study exploring relationships between multiple cellular inputs and a molecule responsible for cell-fate decisions, ii) a many-many study exploring the relationships between multiple cytokines and multiple downstream transcription factors, and iii) a one-to-one study to showcase the ability to recover a cancer related association, viz. the Warburg effect, from past literature. The storytelling pipeline helps narrow down a scientist's focus from several hundreds of thousands of relevant documents to only around a hundred stories. We argue that our approach can serve as a valuable discovery aid for hypothesis generation and connection exploration in large unstructured biological knowledge bases.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0029509en
dc.identifier.eissn1932-6203en
dc.identifier.issue1en
dc.identifier.othere29509en
dc.identifier.pmid22235301en
dc.identifier.urihttp://hdl.handle.net/10919/85595en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleConnecting the Dots between PubMed Abstractsen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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