(Recorded on November 13, 2014)

Sponsored by
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It is no surprise that authors place what they consider to be their most important and promising findings into an abstract. But as an abstract’s size is very limited, many discoveries and observations are excluded, and only published within the body of full-text. Among them are detailed descriptions of methodology, negative statements, results that authors considered to be less relevant or less fitting the main idea of publication.

This session will review the results of our study to estimate the number and value of target-related facts that are missing from abstracts or were published in abstracts much later than in full-text.

Learning Objectives:

  • View results of a study comparing the text mining results of full-text articles vs. abstracts
  • Learn about the value of full-article text mining
  • Lean about considerations when performing text mining for early drug discovery

Speaker: Jaqui Hodgkinson PhD, Vice-President of Product Development

Elevier Jaqui

Jaqui studied for a BSc in molecular biology at the University of Durham, England. On graduating in 1992, she was awarded a UK Medical Research Council PhD grant and moved to the Biochemistry department, University of Oxford, England. In 1996, Jaqui moved to Glaxo Wellcome, London, where she worked as a medical data scientist in the GI group. In 1997, she relocated to the Netherlands, initially working at Solvay Pharmaceuticals. During this time she worked on Solvay’s Cardiovascular portfolio in the Netherlands, France and Germany, and she was also involved in setting up their new R&D medical writing group.

Jaqui joined Elsevier in 1999 as Program Manager large global Pharma accounts, she went on to build a large team of publication specialists in the cardiovascular area. She now heads up the Elsevier Life Science Solutions biology product teams, currently working on Pathway Studio, Pharmapendium and text mining. She is particularly interested in developing use case driven product workflows, and external data integration.