Instructors
Nikta is a PhD student in the Medical Biophysics program at the University of Toronto. She completed her Bachelor of Science in Microbiology and her Master of Science in Bioinformatics. For her MSc thesis, Nikta worked on developing supervised algorithms for classifying cancer-specific somatic mutations. Her research interests include application of machine learning algorithms in pharmacogenomic analysis, cancer diagnosis and personalized medicine.
The goal of Professor Basu’s research is to design, validate, and apply innovative and sustainable approaches (focused on toxicogenomics) to address the most pressing societal concerns over toxic chemicals in our environment. Professor Basu’s research is multidisciplinary (bridges environmental quality and human health), inter-sectoral (most projects driven by stakeholder needs, notably government and communities), and driven by environmental justice concerns.
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.
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.
Rob (or “Dr. Robert Beiko”, if you want to be all formal about it) is an Associate Professor and Canada Research Chair in Bioinformatics in the Faculty of Computer Science at Dalhousie University. Before coming to Dal in 2006, he was a postdoc in the lab of Mark Ragan at the University of Queensland in Brisbane, Australia. And before that, he completed a PhD in Biology at the University of Ottawa (1998-2003). Although all of his formal training was in biology, an interest in machine-learning approaches, algorithms for identifying important evolutionary events, and visualization of biological data have ultimately led him to put down stakes in Computer Science and collaborate with some of the best in the business here.
After completing a postdoctoral position in Australia, Rob joins the team as a Bioinformatics consultant specializing in fungal/plant genetics and developing omics resources, strategies, and infrastructure for laboratories working with non-model organisms. He has more than a decade of experience working in genomics, and holds a PhD in bioinformatics from Curtin University.
Robin is a microbiologist with experience in bioinformatics, curation and outreach. Dr. Haw has a PhD in genetics and was a senior curator at Biomolecular Interaction Network Database (BIND) and managing curator at Science Signaling’s Signal Transduction Knowledge Environment (STKE). He has been responsible for coordinating outreach, Reactome presentations and training at workshops and conferences.
Ruiyan is a PhD candidate in the Department of Medical Biophysics at the University of Toronto. Graduated with a Bachelor’s degree in Biomedical Engineering from the Hong Kong Polytechnic University, she is interested in applying advanced deep learning techniques to medical image classification and segmentation in radiotherapy. Her current research focuses on automatic segmentation of targets and organs-at-risk in cervical brachytherapy using deep learning.
Bioinformatician and data analyst in the Bader lab applying pathway and data analysis to varied data types. Developed Enrichment Map App for Cytoscape, an app to visually translate functional enrichment results from popular enrichment tools like GSEA to networks. Further developed the Enrichment Map Pipeline including development of additional Apps to help summarize and analyze resulting Enrichment Maps, including PostAnalysis, WordCloud, and AutoAnnotate App.