A new paper entitled Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome was recently published in STAR Protocols (Cell Press). This paper accompanies the MethylSight paper published in July and describes how the MethylSight lysine methylation predictor can be leveraged to identify novel methylated lysines for validation by mass spectrometry.

It is with great pride that I can announce that the paper Proteome-wide Prediction of Lysine Methylation Reveals Novel Histone Marks and Outlines the Methyllysine Proteome that I co-first authored with my co-supervisor, Kyle Biggar, was published in the peer-reviewed journal Cell Reports. In this paper, we present MethylSight, a machine learning model trained to predict lysine methylation in human proteins.

Today, my thesis advisor, Jim Green, was interviewed by CBC Radio One (Ottawa) about our COVID-19 research project. In the interview, Jim explains how we are using methods such as genetic algorithms and protein-protein interaction predictors to design novel peptides that can hopefully disrupt interactions between SARS-CoV-2 viral proteins and human proteins. This collaborative work involves Jim, fellow PhD candidate Kevin Dick, my co-advisor Kyle K. Biggar, a couple of his graduate students, and myself.

I am happy to announce that I successfully passed my PhD comprehensive examination which covered a variety of topics in machine learning and artificial intelligence. I like to joke that this has left permanent grill marks! Time to get back to work! 👨🏾‍💻

It is with great pleasure that I announce that I was awarded the Douglas Millar scholarship (3,200CAD) awarded annually to an outstanding graduate student in engineering.