Revolutions in medical technology that promise patient-centered, personalized medicine are producing an unfathomable amount of data. New techniques that identify previously unknown organisms in our environment, and even in our bodies, are expanding our knowledge of the complexities of ecosystems and living organisms. As the data grows exponentially, our user-friendly tools help research scientists, healthcare professionals, and policymakers distill this information down to manageable and actionable relationships.
One of the distinguishing capabilities of our team is the organization and presentation of biological data. Our experience includes bringing together the most current, gold-standard knowledge about organisms, their genes and genomes, and the way they interact together in response to different stimuli into databases that are easily queried. Having the data structured in this way enables analyses that span from small-scale studies of select organisms, to broad, systems-wide approaches that can combine thousands of datasets. We have developed computational methods that allow us to identify patterns and correlations across broad categories that include exploring biochemical networks, identifying the metabolites or genes associated with specific diseases, and developing insight into the evolution of different organisms. We have used machine learning in combination with this data to identify biological threats and predict specific disease phenotypes.