The DSI is excited to announce funding support to Drs. Artem Cherkasov and Faraz Hach from the Vancouver Prostate Centre for their COVID-19 therapeutic discovery project. This project seeks to use deep learning (DL) techniques to identify potential therapeutic agents to target one of the main proteins (Mpro/3CLpro) that enable the SARS-CoV-2 virus to replicate. A description of the project follows below.

Drs. Cohen-Freue (Statistics) and Blydt-Hansen (Pediatrics) were awarded funding for their project on applying statistical models (i.e., classification) to identify a metabolomic signature for personalized risk assessment in pediatric patients receiving a transplated kidney. The funds from the DSI Postdoctoral Matching Fund Program will enable the team to apply and extend various machine learning methods to build a metabolomics classifer to predict long-term outcome (i.e., allograft survival).

The DSI is excited to annouce that Drs. Ipek Oruc and Ozgur Yilmaz are awarded funding for their project on deep learning models for analyzing retinal images for medicial diagnosis and prediction. The funds from the DSI Postdoctoral Matching Fund Program will allow this research team to extend their work with deep neural networks to make predictions (i.e., classification) on various medical disorders ranging from neurological diseases to cancer using retinal images integrated with electronic medical records.

Developmental psychology fundamentally relies on the robust measurement of individual differences - capturing variables at an individual participant level - in order to formulate hypotheses about what children know, how their psychology changes over time, and to characterize the best predictors of their long-term educational, health, and psychological outcomes.

Parallel advances in high-throughput sequencing and high performance computing now allow us to produce a tremendous amount of genome-wide biological data at the genome, epigenome, and transcriptome levels at multiple cellular resolutions. By combining these data, we have an unprecedented opportunity to derive a mechanistic understanding of biological systems and identify causal factors that lead to human disease.

Tuberculosis (TB) is still a problem in British Columbia, with approximately 250 cases diagnosed each year. In order to meet the WHO’s goal of achieving TB pre-elimination by 2030, TB rates in BC need to decline at a faster rate, and a change in how we manage TB prevention and care in the province is needed. Fortunately, all TB-related laboratory, epidemiology, clinical, and public health activities are centralized at the BC Centre for Disease Control (BCCDC).

Reliable detection of disease in the early stages of Alzheimer’s Disease (AD) continues to be a challenge. This project led by Drs. Conati and Field aims to investigate the value of eye-tracking data as one of the sources of information to build machine learning detectors of AD. In addition, the team will investigate eye-tracking based detectors of AD-related states such confusion and distress during naturally occurring tasks.

Extraction of knowledge from information sources ranging from unstructured and semi-structured, to structured has gained significant interest both in academia and in the industry. This is fueled by applications such as question answering and computational fact checking. Knowledge graphs (KG) have lately emerged as a de facto standard for knowledge representation, whereby knowledge is expressed as a collection of “facts", represented in the form of (subject, predicate, object) triples where subject and object are entities and predicate is a relation between those entities.

In this project, Drs. Sigal and Schmidt are pursuing a number of research goals at the intersection of computer vision and machine learning. In part one, the team will advance automatic video summarization by exploring novel richer joint video-linguistic and graph-structured representations to facilitate video retrieval, summarization and--potentially--action recognition tasks.