Nicholas P. Tatonetti, PhD
- Assistant Professor of Biomedical Informatics
Credentials & Experience
Education & Training
- BS, Computational Mathematics, Arizona State University
- BS, Molecular Biosciences/Biotechnology, Arizona State University
- MS, Biomedical Informatics, Stanford University
- PhD, Biomedical Informatics, Stanford University
Honors & Awards
- New Investigator Award, American Medical Informatics Association (AMIA), November 2016
- Irving Scholars Award, Irving Institute for Clinical and Translational Research ($180,000), July 2015
- Kavli Foundational Fellow, May 2014
- PhRMA Foundation Early Career Award ($100,000), January 2014
- Featured by Genome Web in the Seventh Annual Young Investigator Profiles, December 2012
- Highlighted Research in the IMIA Yearbook of Medical Informatics, 2012
- Top Podium Presentation Award - AMIA Summit on Translational Bioinformatics, March 2011
- Best Presentation (Runner Up) - Biomedical Computation at Stanford, November 2010
- Outstanding Paper Award - AMIA Summit on Translational Bioinformatics, March 2010
- Department of Energy Graduate Fellowship ($150,000), September 2010
- National Library of Medicine Training Grant Recipient, September 2008
- Phi Beta Kappa Lifetime Member, November 2007
- Beckman Scholar Award ($17,600), May 2007 - October 2008
- Goldwater Scholar Honorable Mention, April 2007
- ASU SOLUR Researcher Award, August 2006 - May 2007
- 1997 Pinto Horse Association of America National Champion, All Around-Year End Youth High Point Award
- 1997 Pinto Horse Association of America National Youth Reining Horse Champion
We are making drugs safer through the analysis of data. Everyday millions of us or our loved ones take medications to manage our health. We trust in these prescriptions to improve our lives and give us hope for a healthier future. Often, however, these drugs have harmful side effects or dangerous interactions. Adverse drug reactions are experienced by millions of patients each year and cost the healthcare industry billions of dollars. In the Tatonetti Lab we use advanced data science methods, including artificial intelligence and machine learning, to investigate these medicines. Using emerging resources, such as electronic health records (EHR) and genomics databases, we are working to identify for whom these drugs will be safe and effective and for whom they will not.
DATA-DRIVEN DRUG DISCOVERY: INVESTIGATING THE MOLECULAR MECHANISMS OF SAFETY AND EFFICAC (Federal Gov)
May 1 2019 - Apr 30 2024
PILOT STUDY PROPOSAL FOR THE CLINICAL AND GENETIC CHARACTERIZATION OF PULMONARY ARTERIAL HYPERTENSION (PAH) (P&S Industry Clinical Trial)
Dec 20 2018 - Dec 20 2023
NEW YORK CITY CONSORTIUM FOR PRECISION MEDICINE (Federal Gov)
Mar 24 2018 - Feb 28 2023
ADVANCED DEVELOPMENT AND DISSEMINATION OF EMERSE FOR CANCER PHENOTYPING FROM MEDICAL RECORDS (Federal Gov)
Aug 17 2017 - Mar 31 2022
COLUMBIA/CORNELL/HARLEM HOSPITAL PRECISION MEDICINE INITIATIVE HPO (Federal Gov)
Jul 6 2016 - Dec 31 2021
DATA-DRIVEN SUBTYPING TO FIND PATIENTS WITH DRUG INTERACTIONS LEADING TO STROKE (Federal Gov)
Sep 1 2018 - Aug 31 2021
CENTERS FOR CANCER SYSTEMS THERAPEUTICS (CAST) (Federal Gov)
Aug 8 2016 - Jul 31 2021
CANCER CENTER SUPPORT GRANT (Federal Gov)
Jul 1 2014 - Jun 30 2020
DRUG EFFECT DISCOVERY THROUGH DATA MINING AND INTEGRATIVE CHEMICAL BIOLOGY (Federal Gov)
Aug 1 2014 - Apr 30 2020
HYLOFIT-DBMI EQUINE DATA SCIENCE (Private)
Aug 15 2019 - Dec 31 2019
BIOMEDICAL DATA TRANSLATOR TECHNICAL FEASIBILITY ASSESSMENT AND ARCHITECTURE DESIGN (Federal Gov)
Sep 25 2016 - Dec 31 2019
THE GENETIC ORIGINS AND COMPLICATIONS OF URINARY TRACT ABNORMALITIES (Federal Gov)
Sep 24 2014 - Sep 24 2019
ASSESSING GENETIC SUSCEPTIBILITY TO DRUG-INDUCED LIVER INJURY THROUGH MINING OF ELECTRONIC HEALTH RECORDS (Private)
Jul 6 2017 - Jul 5 2018
I3DEAL FOR HIV RESEARCH (Federal Gov)
May 13 2014 - Oct 31 2016
WICER 4U (Federal Gov)
Sep 30 2013 - Sep 30 2015
RESEARCH STARTER GRANT IN INFORMATICS (Private)
Jan 1 2014 - Dec 31 2014
- Hao, Yun, Kayla Quinnies, Ronald Realubit, Charles Karan, and Nicholas P. Tatonetti. 2018. “Tissue‐Specific Analysis of Pharmacological Pathways.” CPT: Pharmacometrics & Systems Pharmacology 7 (7). John Wiley and Sons Inc. 453–63. doi:10.1002/psp4.12305.
- Tatonetti, Nicholas P. 2018. “The next Generation of Drug Safety Science: Coupling Detection, Corroboration, and Validation to Discover Novel Drug Effects and Drug-Drug Interactions.” Clinical Pharmacology and Therapeutics 103 (2): 177–79. doi:10.1002/cpt.949.
- Alexandre Yahi, Rami Vanguri, Noemie Elhadad, Nicholas P Tatonetti, Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories 31st Conference on Neural Information Processing Systems (NIPS 2017) December 2017
- Nenad Macesic, Fernanda Polubriaginof, Nicholas P Tatonetti Machine learning: novel bioinformatics approaches for combating antimicrobial resistance Current opinion in infectious diseases December 2017
- Robert Moskovitch, Fernanda Polubriaginof, Aviram Weiss, Patrick Ryan, Nicholas Tatonetti Procedure prediction from symbolic electronic health records via time intervals analytics Journal of biomedical informatics November 2017
- Nicholas P Tatonetti, Translational medicine in the Age of Big Data Briefings in Bioinformatics October, 2017
- Boland, Mary Regina, Fernanda Polubriaginof, and Nicholas P. Tatonetti. 2017. “Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect.” Scientific Reports 7. London: Nature Publishing Group UK: 12839. doi:10.1038/s41598-017-12943-x.
- Boland, Mary Regina, Pradipta Parhi, Pierre Gentine, and Nicholas P. Tatonetti. 2017. “Climate Classification Is an Important Factor in Assessing Quality-of-Care Across Hospitals.” Scientific Reports 7. London: Nature Publishing Group UK: 4948. doi:10.1038/s41598-017-04708-3.
- Moskovitch, Robert, Hyunmi Choi, George Hripsack, and Nicholas Tatonetti. 2017. “Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 14 (3): 555–63. doi:10.1109/TCBB.2016.2591539.
- Boland, Mary Regina, Konrad J. Karczewski, and Nicholas P. Tatonetti. 2017. “Ten Simple Rules to Enable Multi-Site Collaborations through Data Sharing.” PLoS Computational Biology 13 (1). San Francisco, CA USA: Public Library of Science: e1005278. doi:10.1371/journal.pcbi.1005278.
- Hao, Yun, and Nicholas P. Tatonetti. 2016. “Predicting G Protein-Coupled Receptor Downstream Signaling by Tissue Expression.” Bioinformatics 32 (22). Oxford University Press: 3435–43. doi:10.1093/bioinformatics/btw510.
- Boland, M R, and N P Tatonetti. 2016. “Investigation of 7-Dehydrocholesterol Reductase Pathway to Elucidate off-Target Prenatal Effects of Pharmaceuticals: A Systematic Review.” The Pharmacogenomics Journal 16 (5). Nature Publishing Group: 411–29. doi:10.1038/tpj.2016.48.
- Chang, Jeremy B., Kayla M. Quinnies, Ronald Realubit, Charles Karan, Jacob H. Rand, and Nicholas P. Tatonetti. 2016. “A Novel, Rapid Method to Compare the Therapeutic Windows of Oral Anticoagulants Using the Hill Coefficient.” Scientific Reports 6. Nature Publishing Group: 29387. doi:10.1038/srep29387.
- Jacunski, Alexandra, Scott J. Dixon, and Nicholas P. Tatonetti. 2015. “Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.” Edited by Lilia M. Iakoucheva. PLoS Computational Biology 11 (10). San Francisco, CA USA: Public Library of Science: e1004506. doi:10.1371/journal.pcbi.1004506.
- Lorberbaum, Tal, Mavra Nasir, Michael J. Keiser, Santiago Vilar, George Hripcsak, and Nicholas P. Tatonetti. 2015. “Systems Pharmacology Augments Drug Safety Surveillance.” Clinical Pharmacology and Therapeutics97 (2): 151–58. doi:10.1002/cpt.2.
- Vilar, Santiago, Eugenio Uriarte, Lourdes Santana, Tal Lorberbaum, George Hripcsak, Carol Friedman, and Nicholas P Tatonetti. 2014. “Similarity-Based Modeling in Large-Scale Prediction of Drug-Drug Interactions.” Nature Protocols 9 (9): 2147–63. doi:10.1038/nprot.2014.151.
- Karczewski, Konrad J., Michael Snyder, Russ B. Altman, and Nicholas P. Tatonetti. 2014. “Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association.” Edited by Greg Gibson. PLoS Genetics 10 (2). San Francisco, USA: Public Library of Science: e1004122. doi:10.1371/journal.pgen.1004122.
- Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease, December 27, 2013
- High-Throughput Methods for Combinatorial Drug Discovery, October 27, 2013
- KARCZEWSKI, KONRAD J., ROBERT P. TIRRELL, PABLO CORDERO, NICHOLAS P. TATONETTI, JOEL T. DUDLEY, KEYAN SALARI, MICHAEL SNYDER, RUSS B. ALTMAN, and STUART K. KIM. 2012. “INTERPRETOME: A FREELY AVAILABLE, MODULAR, AND SECURE PERSONAL GENOME INTERPRETATION ENGINE.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 339–50.
- Tatonetti, Nicholas P, Guy Haskin Fernald, and Russ B Altman. 2012. “A Novel Signal Detection Algorithm for Identifying Hidden Drug-Drug Interactions in Adverse Event Reports.” Journal of the American Medical Informatics Association : JAMIA 19 (1). BMA House, Tavistock Square, London, WC1H 9JR: BMJ Group: 79–85. doi:10.1136/amiajnl-2011-000214.
Karczewski, Konrad J., Nicholas P. Tatonetti, Stephen G. Landt, Xinqiong Yang, Teri Slifer, Russ B. Altman, and Michael Snyder. 2011. “Cooperative Transcription Factor Associations Discovered Using Regulatory Variation.” Proceedings of the National Academy of Sciences of the United States of America 108 (32). National Academy of Sciences: 13353–58. doi:10.1073/pnas.1103105108.
Tatonetti, Nicholas P, Tianyun Liu, and Russ B Altman. 2009. “Predicting Drug Side-Effects by Chemical Systems Biology.” Genome Biology 10 (9). BioMed Central: 238. doi:10.1186/gb-2009-10-9-238.