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  • Reciprocal Perspective: A Cascaded Semi-Supervised Machine Learning Framework to Improve Pairwise Classification & Regression

    Resource Type:
    Thesis
    Creator:
    Dick, Kevin
    Abstract:
    Many real-world problems can be represented as a network, with nodes representing elements and the edges (i.e. links; or lack thereof) capturing the relationship between elements. An example domain that leverages link prediction algorithms to elucidate relationships between pairs of nodes is the task of protein-protein interaction (PPI) prediction. Leveraging high-performance computing and optimized PPI predictors, it is recently possible to evaluate every possible combination of paired nodes enabling the generation of a comprehensive prediction matrix (CPM). We introduce a novel semi-supervised machine learning method, denoted Reciprocal Perspective (RP), which leverages this new wealth of information by extracting context-based features from this CPM by considering reciprocal views of pairwise elements for use in a cascaded classifier which has demonstrated significant improvement in predictive performance. Historically, this achievable wealth of information has been ignored due to computational intractability. We demonstrate that expending compute resources to generate CPMs is a worthy investment given the improvement in predictive performance in both classification- and regression-type tasks. This thesis makes contributions at all stages of a prototypical prediction pipeline. We demonstrate that RP is applicable to a variety of application domains within bioinformatics (PPI, microRNA-target, and drug-target interaction prediction) as well as within Network Science with Recommendation Systems. Furthermore, RP is demonstrated to improve individual model performance as well as function as an ensemble method to combine multiple experts. Taken together, these contributions demonstrate that RP can be broadly applied for pairwise prediction problems across different domains, problem formulations, and varying scales of data.
    Thesis Degree:
    Doctor of Philosophy (Ph.D.)
    Thesis Degree Discipline:
    Engineering, Biomedical
    Date Created:
    2022

  • Canadian jobs amid a pandemic: examining the relationship between professional industry and salary to regional key performance indicators

    Resource Type:
    Report
    Creator:
    Patsula, Michael, Tran, Christopher, Wang, Christopher, Khalil, Hoda, Dick, Kevin, Melone, Benjamin, Wainer, Gabriel, and Anilkumar, Rahul
    Abstract:
    The COVID-19 pandemic has contributed to massive rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a “snapshot” of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indicators (e.g. quality of life index, cost of living) for major cities across Canada. This analysis aims to help Canadians make informed career decisions, collect a “snapshot” of the Canadian employment opportunities amid a pandemic, and inform job seekers in identifying the correct fit between the desired lifestyle of a city and their career. We collected a new high-quality dataset of job postings from jobbank.gc.ca obtained with the use of ethical web scraping and performed exploratory data analysis on this dataset to identify job opportunity trends. When optimizing for average salary of job openings with quality of life, affordability, cost of living, and traffic indices, it was found that Edmonton, AB consistently scores higher than the mean, and is therefore an attractive place to move. Furthermore, we identified optimal provinces to relocate to with respect to individual skill levels. It was determined that Ajax, Marathon, and Chapleau, ON are each attractive cities for IT professionals, construction workers, and healthcare workers respectively when maximizing average salary. Finally, we publicly release our scraped dataset as a mid-pandemic snapshot of Canadian employment opportunities and present a public web application that provides an interactive visual interface that summarizes our findings for the general public and the broader research community.
    Date Created:
    2022-06-08