I am interested in leveraging computational methods and high-performance computing infrastructure to provide insight into complex problems including the elucidation of protein-protein interactions and binding interfaces, analysis of autonomous vehicle-sensed data, scientometrics, and the promotion of access to information.
My thesis is focused on the development of a novel semi-supervised machine learning method, however as a polymath and solutionist, I am involved in a diverse array of research projects. I am passionate about scientific communication and am currently an executive editor of the Health Science Inquiry.
PhD in Biomedical Engineering (Data Science & Bioinformatics), 2020
BSc in Biology & Computer Science, 2014
MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA–gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved (𝑝<0.001) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA–gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA–gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.