Graciela Gonzalez


Biomedical Informatics, Arizona State University

Assistant Professor, Department of Biomedical Informatics

Professor Gonzalez is an assistant professor at the Department of Biomedical Informatics (BMI) at Arizona State University, and data core director for one of the largest National Institutes of Health/ National Institute on Aging supported Alzheimer’s Disease Centers. She is also a member of the National Library of Medicine’s only chartered review committee — the Biomedical Library and Informatics Research Committee. She leads the Discovery through Integration and Extraction of Genomic knOwledge (DIEGO) lab, in the area of knowledge discovery, focusing her research on translational applications of information extraction using natural language processing techniques. She has successfully led teams of students placing at the top of the Biocreative II.5 and Biocreative III biomedical NLP challenges as well as the i2v2/VA clinical NLP challenge.


Work in the DIEGO lab spans the spectrum from data extraction (concepts and relationships) to data integration, modeling and advanced analysis to facilitate discovery from “bench to bedside.” From transcription factor binding site pattern discovery, through gene prioritization for drug and disease-related studies, to patient cohort management and adverse drug reaction surveillance, the lab has contributed to the advancement of knowledge discovery methods across the biomedical spectrum.

In September of 2012, Professor Gonzalez was granted funding from the National Library of Medicine for a total of 1.4 million dollars over 4 years for a project focusing on how to best extract and use information about adverse drug reactions that is posted by patients and their caregivers on web sites and social networks.

She recently completed research funded also by the National Library of for the collaborative development of semantic methods to find mentions of medical problems and treatments in free text portions of clinical records, as well as different kinds of relationships between them (such as “treatment causes medical problem” or “treatment administered for medical problem”). This work, in collaboration with colleagues at the Mayo Clinic Rochester and the University of Texas Medical School in Houston can be relevant to use cases whereby the extracted knowledge is applied to facilitate patient cohort selection for clinical research.

Major advances have also been made in the DIEGO lab towards providing molecular genomic researchers with a usable gene prioritization tool that integrates data from curated databases, the biomedical literature and specific assays to go beyond purely statistical analysis of experimental results.

Phone: 480-884-0240