Modern biomedical sciences are data-driven, and depend on many reliable databases of biomedical knowledge, or simply “knowledge bases”. Knowledge bases are particularly important for precision medicine, where the idea is to treat patients with the same condition differently according to their genetic profiles. Because the number of possible genetic profiles is huge, knowledge bases are essential for clinicians to look up what treatment is known to be effective for their patients. Biomedical Text Mining and Natural Language Processing (NLP) have been touted as the solution to extract the knowledge from research publications and automatically create the knowledge bases to meet the needs. However, it is still far from practical to fully automate the curation. In this talk, I will present a hybrid human-AI solution with web annotation and its use by ClinGen for the curation for ClinVar, a widely used knowledge base of disease-gene associations.
Chun-Nan Hsu, PhD, Associate Professor at the School of Medicine, University of California, San Diego. Dr. Hsu has published more than 100 highly cited peer-reviewed research articles in the fields of machine learning, data mining, and biomedical informatics. His team developed widely used software tools for biomedical sciences, leading to commercialized products. He was awarded Senior Member of Association of Computing Machinery (ACM) in 2011 and the IBM Faculty Award for his distinguished contributions to biomedical text mining in 2012.