Our research focuses on extracting and encoding information from biomedical free-text, including clinical reports and the biomedical literature. We are a small but diverse lab with training in linguistics, computer science, artificial intelligence, health services research, medicine, and biomedical informatics.
My research interests are centered on organizing biological data in ways that make it more amenable to computation. I am particularly interested in the development of ontologies that describe biological knowledge, and provide a means for detailed analysis of associated data.
Facelli’s research interests are centered in the application of advance computing techniques to solve important problems in the biomedical domain. The projects in his research group use similar computational infrastructure and tools to maximize the synergy among projects, benefiting the students, postdocs and faculty in the group who are exposed to a variety of biomedical problems that are addressed by a common set of computational approaches.
We are particularly interested in a bioinformatics approach in order to understand genome variation in complex human disease and develop research infrastructure for characterizing biological mechanisms connecting genotype to phenotype, with emphasis on alternative splicing as an important source of molecular diversity.
The research in our laboratory is focused on the application of computational methods to develop a deeper understanding of genetic variation in diverse contexts. We develop such methods so that we and others may apply them to experiments investigating the impact of genetic variation on human disease, evolution, and somatic differentiation. Genome research is difficult - we strive to develop computational means that make it easier.
Our research focuses on using informatics techniques — particularly natural language processing –- to address research questions in population health. Working with national and international collaborators from psychology, information science, computer science, bioethics, and public health, we develop tools, methods, and linguistic resources designed to support the analysis of existing text data, with the overarching goal of better understanding health behavior and health outcomes at the population level.
We create and study methods and tools to support unstructured clinical data reuse. Reuse of clinical data is essential to fulfill the promises for high quality healthcare, improved healthcare management, and effective clinical research. Accurate and detailed clinical information, as found in patient Electronic Health Records (EHR), rather than existing but often biased and insufficiently detailed diagnostic and procedure codes assigned for reimbursement and administrative purposes only are needed for effective clinical research and high quality and efficient healthcare.