Macrobiology: The redundancy and pleiomorphy of most physiologies implies any single set of processes and genes are unlikely to capture the entirety of the physiology operating in all contexts. Models of physiology that are defined from the bottom-up do not work very well for this reason, even if the mountains of data required to define these models are obtained. Also, purely statistical or data driven perspectives fail to capitalize on the enormous trove of prior knowledge gleaned in the biological sciences. Macrobiology leverages this prior knowledge in the interpretation of whole physiologies (and pathologies) using empiircal grounding offered by high-throughput, comprehensive measurements. |
Clinical and Public Health Informatics. We study the application of medical informatics, computer science, epidemiology, and biostatistics to
improve public health and clinical practice. Our public health informatics research focuses on biosurveillance and population
health monitoring, influenza and infectious disease epidemiology, pharmacosurveillance, and geographical information systems (GIS).
Our clinical informatics research focuses on personally controlled health records, clinical prediction and decision making,
and advanced health communication infrastructures. We also develop practical technologies deployed in real-world settings, including
the AEGIS real-time surveillance system and the IndivoHealth personally controlled electronic medical record.
Genome-scale measurements have served as a disruptive technology for biomedical research in a the analytic approach that these new measurement modalities require and the risks inherent in misunderstanding their nature. Early on, we demonstrated an impressively poor correlation of expression of microarrays from different manufacturers, and even across the generations of a platform for a single manufacturer. Substantial difficulties in the annotations of common oligonucleotide microarrays were noted and methods to overcome these were provided. When combining these measurements to create prognostic algorithms (as the Food and Drug Administration has already done for breast cancer recurrence), we have identified challenges in using data from real-world trials (e.g. from censored data) and also subtleties in how to weight the contributions of pathways to particular pathologies. |
Informed Cohort: In the name of privacy and protection of study subjects, the research community has, albeit with good intentions, broken the historical doctor-patient compact, distorting an ideal of information exchange that might inform subjects of health risks or benefits. The advent of genome-scale measurements and health information technologies allow us to reconnect patient subjects and researchers in a manner respectful of regulations and privacy concerns, yet maximizing potential benefit to the public and the individual in the course of research. We term this solution the Informed Cohort. |
