The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. But the large body of knowledge—mostly exists as free text in journal articles for humans to read—presents a grand new challenge: individual scientists around the world are increasingly finding themselves overwhelmed by the sheer volume of research literature and are struggling to keep up to date and to make sense of this wealth of textual information. Our research aims to break down this barrier and to empower scientists towards accelerated knowledge discovery. We will discuss our work on developing sharable resources (e.g. software tools and corpora) as well as their uses in real-world applications.
After participating in this activity, the learner should be better able to:
- Identify publicly available NCBI text mining tools and services
- Understand needs and challenges in developing interoperable and scalable tools
- Discuss applications of text mining research in accelerating knowledge discovery
Zhiyong Lu, PhD
Earl Stadtman Investigator
Head, Biomedical Text Mining Group
NCBI, NLM, NIH
Dr. Lu is Earl Stadtman investigator at NCBI, NLM/NIH where he leads the text mining research group. His research focuses on developing computational methods for analyzing and making sense of natural language data in biomedical literature and clinical text. Several of his recent research has been successfully adopted in PubMed/PMC and other community resources like SwissProt. Dr. Lu is an Associate Editor for BMC Bioinformatics and serves on the editorial board for the Journal Database. He is also an organizer of the BioCreative challenge and has authored over 100 publications.