Translational Medicine in the Age of Data

Billions of clinical measurements are recorded every day and stored in electronic health systems around the world. Each one of these experiments is a window into the human system, creating the most comprehensive and diverse medical data set ever imagined. Unfortunately, traditional statistical techniques were not developed to handle such diversity, instead they excel at analyzing homogenous data sets with first order effects. Because of this, these techniques are simply unable to untangle the sophisticated web of biological pathways and genetic interactions governing the human system.

With enormous data come enormous opportunity

Data Science is a new field dedicated to developing the methods, algorithms, and tools to unravel the complexities of enormous data. In our lab we advance data science by designing rigorous computational and mathematical methods that address the fundamental challenges of health data science. Foremost, we integrate our medical observations with systems and chemical biology models to not only explain drug effects, but also further our understanding of basic biology and human disease.

One particular area of interest is the integration of high-throughput data capture technologies, such as next-generation genome and transcriptome sequencing, metabolomics, and proteomics, with the electronic medical record to study the complex interplay between genetics, environment, and disease.

For a more in-depth information on our research areas of interest see our reviews in Science Translational Medicine and Clinical Pharmacology & Therapeutics.

Medicine Gets Personal

By Aliyah Baruchin

"The information in the chart will be used to shape and guide this patient's care. But researchers such as Nicholas Tatonetti, PhD, assistant professor of biomedical informatics and director of clinical informatics for the cancer center, would like to know more-a lot more." Read the whole story.

Featured publication

Santiago Vilar, Eugenio Uriarte, Lourdes Santana, Tal Lorberbaum, George Hripcsak, Carol Friedman, Nicholas P Tatonetti.
Similarity-based modeling in large-scale prediction of drug-drug interactions.
Nature Protocols. August 14, 2014. Source.

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Our lab is in the Department of Biomedical Informatics at Columbia University as well as the Department of Systems Biology, and the Department of Medicine.

Potential graduate students should apply to the Department of Biomedical Informatics Training Program or the Computational Biology Training Program at Columbia.