People
Dr. Griffith’s research is focused on the development of personalized medicine strategies for cancer using genomic technologies. He develops and uses bioinformatics, machine learning and clinical statistics for the analysis of high throughput sequence data and identification of biomarkers for diagnostic, prognostic and drug response prediction. He has led the development of key online informatics resources such as DGIdb, CIViC, GenVisR and more.
Patrick McMillan is a PhD candidate studying bioinformatics at the University of Guelph. His research looks at how to better model the interaction between a crop’s genotype and the environment in which it’s grown. Patrick completed his M.Sc. in Applied Statistics where he studied how to model the effects of surface mining on aquatic ecosystems in the Athabasca Oil Sands region in Alberta.
I received a PhD in biomolecular modelling from University College London, researched protein folding and genomics at UCSF and Yale, and am currently a professor of computational biology, focusing on development of techniques to analyze low-complexity and intrinsically disordered proteins, and prions.
Dr. Paul Pavlidis is a Professor of Psychiatry and in the Michael Smith Laboratories at UBC. His lab’s primary research focus is understanding the molecular basis of neurodevelopmental and neuropsychiatric disorders using computational and bioinformatics methods to analyze genomics data. Dr. Pavlidis obtained his BA in biochemistry from Cornell University and did his PhD on neurogenetics and neurophysiology at the University of California, Berkeley. He did postdoctoral work on the molecular basis of synaptic plasticity in rodents before shifting focus to computational biology and genomics. He was on the faculty of Columbia University’s department of Biomedical Informatics prior to moving to UBC, where he has been since 2006. Dr. Pavlidis has a long track record of computational method and tool development in functional genomics, especially in transcriptomics and gene network analysis, and frequently collaborates with basic and clinical researchers.
Dr. Stothard’s research group uses bioinformatics, whole-genome sequencing and other genomics technologies to identify causative or informative sequence variants that can be used to improve animal breeding, management, and conservation approaches. His group has also developed and maintains several popular tools for characterizing and visualizing microbial genomes.
Qian Lin is a systems neuroscientist who studies the neural computation of cognition, by whole-brain single-neuron recordings in behaving zebrafish. Before joining UofT, she was a Leon Levy postdoctoral fellow at the Rockefeller University in NYC and Research Institute of Molecular Pathology in Austria. She got her Bachelor at the University of Science and Technology of China, and PhD at National University of Singapore.
Our lab uses machine learning and artificial intelligence to do biomedical research, focusing on cancer evolution, gene regulation, clinical informatics, and gene function prediction. A key interest is the role of RNA-binding proteins (RBPs) in post-transcriptional regulation. We focus on developing computational and experimental techniques to determine the RNA specificities of RBPs (both sequence and structural) and use these specificities to predict their target transcripts, determine RBP function, and ultimately decipher the regulatory code. Another focus is reconstructing and modelling somatic evolution (pre- and post-cancer) using bulk and single-cell genomic data. In general, we are focused on using large, heterogeneous functional genomic datasets to uncover insights about gene function. Recently, we have becoming increasingly interested in using artificial intelligence and predictive analytics, along with electronic medical records, to inform patient care, particularly in the domain of auto-immune disease.
Our research traverses from genomes to small molecules integrating systems, structural and computational pharmacology as well as chemo- and bioinformatics. Our work is divided into four interconnected but independent axes within which we combine the development and use of innovative computational methods with experimentally validation. Namely: 1. The reconstruction and simulation of metabolic networks; 2. The detection of binding-site structural similarities; 3. Simulation of dynamic aspects of protein function; and 4. The development of docking algorithms.