Nicholas Tatonetti, PhD
Assistant Professor
Columbia University
Dr. Nicholas Tatonetti trained in mathematics and molecular biology at Arizona State University before receiving his PhD in biomedical informatics in 2012 from Stanford University. His dissertation was focused on the development of novel statistical and computational methods for observational data mining. He applied these methods to drug safety surveillance where he discovered and validated new drug effects and interactions. In September 2012, Dr. Tatonetti joined the faculty as an Assistant Professor in the Departments of Biomedical Informatics, Systems Biology, and Medicine. Shortly after, he became Director for the Clinical Informatics Shared Resource (CISR) at the Herbert Irving Comprehensive Cancer Center. His lab at Columbia is focused on expanding upon his previous work in detecting, explaining, and validating drug effects and drug interactions from large-scale observational data.
Widely published in both clinical and bioinformatics, Dr. Tatonetti is passionate about the integration of hospital data (stored in the electronic health records) and high-dimensional biological data (captured using next-generation sequencing, high-throughput screening, and other “omics” technologies). His lab develops the algorithms, techniques, and methods for analyzing enormous and diverse data by designing rigorous computational and mathematical approaches that address the fundamental challenges of observational analysis — bias and confounding. Foremost, they integrate medical observations with systems and chemical biology models to, not only, explain clinical effects, but also further our understanding basic biology and human disease. Dr. Tatonetti has been featured by the New York Times, Genome Web, and Science Careers. His work has been picked up by the mainstream and scientific media and generated hundreds of news articles.
Boyce RD, Ryan PB, Noren GN, et al. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest. Drug Saf. 2014 Jul 2;2:2.
MR Boland, NP Tatonetti, G Hripcsak. CAESAR: a Classification Approach for Extracting Severity Automatically from Electronic Health Records. Intelligent Systems for Molecular Biology Phenotype Day. 2014; Boston, MA.
Disease Risk Factors Identified Through Shared Genetic Architecture and Electronic Medical Records. Science Translational Medicine April 30, 2014
Li Li, David J Ruau, Chirag J Patel, Susan C Weber, Rong Chen, Nicholas P Tatonetti, Joel T Dudley, and Atul J Butte
The Weber Effect and the United States Food and Drug Administration’s Adverse Event Reporting System (FAERS): Analysis of Sixty-Two Drugs Approved from 2006 to 2010. Drug Safety April 4, 2014
Keith B Hoffman, Mo Dimbil, Colin B Erdman, Nicholas P Tatonetti, and Brian M Overstreet
Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association. PLOS Genetics February 6, 2014
Konrad J. Karczewski, Michael Snyder, Russ B. Altman, and Nicholas P. Tatonetti
Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease. Clinical Pharmacology and Therapeutics October 10, 2013
Alexandra Jacunski and Nicholas P. Tatonetti.
High-Throughput Methods for Combinatorial Drug Discovery. Science Translational Medicine October 2, 2013
Xiaochen Sun, Santiago Vilar, and Nicholas P. Tatonetti.
Web-scale pharmacovigilance: listening to signals from the crowd. Journal of the American Medical Informatics Association March 6, 2013
Ryen W White, Nicholas P. Tatonetti, Nigam H. Shah, Russ B. Altman, and Eric Horvitz.
Data-driven prediction of drug effects and interactions. Science Translational Medicine 4, 125ra31 (2012).
Nicholas P Tatonetti, et al.
A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform Assoc (2011)
Nicholas P Tatonetti, et al.
Detecting Drug Interactions From Adverse-Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose Levels Clinical Pharmacology & Therapeutics (2011)
Nicholas P Tatonetti, et al.
Interpretome: A freely available, modular, and secure personal genome interpretation engine. Pac Symp Biocomput 17:339-350(2012)
Konrad Karczewski, Rob Tirrell, Pablo Cordero, Nicholas P Tatonetti, et al.
Cooperative transcription factor associations discovered using regulatory variation. Proceedings of the National Academy of Science USA 108, 13353-13358 (2011).
Konrad Karczewski, Nicholas P Tatonetti, et al.
Predicting drug side-effects by chemical systems biology. Genome Biology (2009) vol. 10 (9) pp. 238
Tatonetti NP, Liu T, Altman RB.
