Translational Bioinformatics 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.
In our lab we develop the algorithms, techniques, and methods for analyzing enormous and diverse data. We do so by designing rigorous computational and mathematical approaches that address the fundamental challenges of observational data analysis -- namely the lack of controlled exposure conditions. And 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.
Konrad J. Karczewski,
Russ B. Altman, and
Nicholas P. Tatonetti.
Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association.
PLOS Genetics. February 6, 2014. Source.
Alexandra Jacunski and Nicholas P. Tatonetti.
Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease.
Clinical Pharmacology and Therapeutics. October 10, 2013. Source.
Xiaochen Sun, Santiago Vilar, and Nicholas P. Tatonetti.
High-Throughput Methods for Combinatorial Drug Discovery.
Science Translational Medicine. October 2, 2013. Source.
Potential graduate students should apply to the Department of Biomedical Informatics Training Program or the Computational Biology Training Program at Columbia.