Paul Pavlidis

Paul Pavlidis

Professor, UBC Department of Psychiatry, Division of Neuroscience and Translational Psychiatry

Associate Director, UBC Graduate Program in Bioinformatics

Member, Djavad Mowafaghian Centre for Brain Health


Short Biography

Dr. Paul Pavlidis is a Professor of Psychiatry based at the Michael Smith Laboratories. He obtained his B.A. in Biochemistry from Cornell University (1989) and his doctorate in Molecular Biology/Neursocence from the University of California, Berkeley (1994). He completed post-doctoral fellowships at Stanford University and Columbia University prior to his appointment as Assistant Professor at Columbia University in 2003. He moved to UBC in 2006 and was promoted to full professor in 2014. His lab focuses on research that is at the intersection of bioinformatics and neuroscience, with a focus on genomics.

Research Focus

Dr. Pavlidis’ research lies at the intersection of bioinformatics and neuroscience. Dr. Pavlidis has a particular interest in neuropsychiatric disorders such as schizophrenia and autism, and how they affect the function of chemical synapses. A current focus of work in his lab involves the large-scale or meta-analysis of functional genomics data (e.g. microarrays). He uses these approaches to study gene networks and their involvement in human neuropsychiatric diseases. To this end Dr. Pavlidis collaborates closely with many laboratory-based neuroscience researchers from UBC and elsewhere. A newer area of interest is in the analysis of neuroanatomical data. Using text mining as well as existing data sources, he is engaged in the analysis of brain structure as it relates to gene expression and the brain “connectome”. As computational biologists, they also generate methods, databases and tools. One of the tools they have developed, “Gemma” is a system that permits researchers to compare and combine gene expression data sets they have generated with other data sets they select from hundreds of other data sets. Gemma will also facilitate the use of other types of data including and proteomics and genetics, and neuroscience-domain-specific data such as neuroanatomy and neuropharmacology.

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