The UBC Data Science Institute is excited to fund a collaboration between Drs. Elodie Portales-Casamar (UBC Department of Pediatrics) and Giuseppe Carenini (UBC Computer Science) titled, "Robust, transferable and interpretable natural language processing of psychiatric clinical notes." The team includes postdoctoral fellow Dr. Ahmed Abura'ed, Dr. Ali Eslami (UBC Department of Psychiatry), and Ali Mussavi Rizi (PHSA).

Drs. Tamara Munzner and Kendall Ho were award DSI funding for their project, "Visual Analytics Support for the HEiDi Virtual PHysician COVID-19 Deployment." This project will leverage advances in data visualization and analytics to optimze the delivery of telehealth care to patients stricken with COVID-19. The outcomes will help health system experts to gain a holistic snapshot of the current care system and expedite analysis and decision-making.

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.

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.

Recent advances in next generation sequencing (NGS) technologies have led to the ability to measure gene expression and DNA mutations across thousands of cells in cancer tumors at the single-cell level. This allows us to quantify the effect of chemotherapeutic drugs on the way tumors mutate and answer questions about why particular groups of cells (known as clones) evade treatment and cause relapse. However, the vast quantities of data produced by such measurements combined with the low signal-to-noise ratio makes analysis and interpretation particularly difficult.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive, debilitating, chronic respiratory disease. It is currently the 4th leading cause of mortality and is responsible for 100,000 hospitalizations and 10,000 deaths annually in Canada, and 3 million deaths worldwide. Although our understanding of COPD pathogenesis has improved substantially over the past 20 years, there is a notable lack of treatments that can modify disease progression and reduce mortality. Furthermore, current methods to clinically diagnose COPD are non-specific and insufficient to advance knowledge.