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Seminar: Towards Personalized Medicine

Dr. Thomas Triplet (Centre for Structural and Functional Genomics, Concordia University)

Friday, March 8, 2013, 10:00 a.m., EV 2.184


The mapping of the human genome, completed in 2003 after 13 years of collective efforts at an estimated cost of 3 billions dollars, had an immense impact on biomedical research. Earlier this year, Life Technologies presented a small device to sequence an entire human genome in a day for less than $1,000, effectively making personal genomics accessible to most laboratories.

Current databases are typically designed as single organism databases and are not readily amenable to complex system-wide research across multiple species. In this talk, I will present the unique challenges of clinical and biological big data, and review the state-of-the-art in genomics data warehousing.

I will also present versatile classification integration and reclassification methods that can combine existing classifications without requiring access to the raw data, and will discuss how they can be leveraged to combine clinical data with omics databases: more accurate predictors for diseases risks and pathologies, integrated with personal omics data, could ultimately lead to early diagnostics and personalized drugs to treat patients given their personal genetic background.


Dr. Thomas Triplet is a postdoctoral researcher at the Centre for Structural and Functional Genomics and the Department of Computer Science and Software Engineering at Concordia University. He is also a member of the professional Ordre des Ingénieurs du Québec. He earned his engineering diploma and Master's degree in Computer Science and Engineering, with distinctions, in 2007 at the French National Graduate School of Engineering ENSICAEN. He completed his Ph.D. in bioinformatics after two years under the supervision of Prof. Peter Revesz at the University of Nebraska-Lincoln, USA, where he was a recipient of an ISEP and a Milton E. Mohr fellowships. His main research interests include the integration and mining of clinical and biological big data for personalized medicine, as well as the visualization and the automated analysis of those data using machine learning.

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