Life science organizations are embracing the digital revolution, but digital transformation demands data transformation. This includes developing strategies to access information buried in unstructured text. Many top pharma and healthcare organizations are using the power of Natural Language Processing (NLP) to transform unstructured text into actionable structured data that can be rapidly visualized and analyzed, for decision support from bench to bedside.
On December 10, CCC will host a webinar with our partners at Linguamatics: “Natural Language Processing for Digital Transformation of Unstructured Text.” In this presentation, Linguamatics will present use cases from pharma, including effective text mining of scientific abstracts and full text papers for rare disease insights.
What will you learn?
- How Natural Language Processing (NLP) text mining can extract relevant structured data from unstructured scientific literature using ontologies, flexible queries and linguistic processing
- How flexible NLP text mining from Linguamatics can provide precise results, quickly
- Real-life success stories from pharmaceutical companies such as Shire, Agios and others who are using NLP to access and gain valuable insights from a variety of unstructured text sources.
About the Presenters
Jane Reed is Director Life Science at Linguamatics, an IQVIA company. She is responsible for developing the strategic vision for Linguamatics’ product portfolio and business development for the pharma and biotech market. Jane has extensive experience in life sciences informatics. She has worked for more than 20 years in vendor companies supplying data products, data integration and analysis and consultancy to pharma and biotech – with roles at Instem, BioWisdom, Incyte, and Hexagen. Before moving into the life science industry, Jane worked in academia with post-doctoral positions in genetics and genomics research
Paul Milligan is Senior Product Manager at Linguamatics and has been with the organization since 2005. In this role he has responsibility for all of Linguamatics products, including I2E and iScite, working with customers to solve their big data and unstructured data problems. Paul has a doctorate from University of Cambridge in computational drug design where his work involved developing algorithms to optimize combinatorial libraries for protein-ligand interactions. After graduating, Paul worked in the Cambridge biotech sector, developing and using bioinformatics and chemoinformatics tools for processing of diverse large-scale databases, including PDB, compound registries and PubMed.
Interested in learning right away? Check out some of these resources: