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By: Cormac Rea

Few experiences inspire panic and fear as much as a time spent in a hospital waiting to be seen for a serious medical procedure.  

Yet, despite ongoing advances in medical science and modelling, patients often remain dependent on limited assessments and data modeling to determine if they even qualify for certain medical interventions.  

Liver transplantation is a critical intervention for patients with end-stage liver disease. But current systems for prioritizing patients on the transplant waitlist create inequities, particularly for women, older patients, and those with some advanced conditions like non-alcoholic steatohepatitis (NASH) or cholestatic liver disease.

Supported by a Data Sciences Institute catalyst seed grant and co-led by investigators’ Rahul G. Krishnan (Assistant Professor, Computer Science and Laboratory Medicine and Pathobiology, Faculty of Arts & Science, Faculty of Medicine, University of Toronto and Faculty Member, Vector Institute) and Mamatha Bhat (Assistant Professor, Division of Gastroenterology, Temerty Faculty of Medicine, University of Toronto and Clinician-Scientist, Multi-Organ Transplant Program, University Health Network), DynaMELD and DynaCOMP is a coordinated effort between clinicians and computer scientists to address specific issues with liver transplant wait-times and patient selection.

“By applying advanced deep learning techniques to large and often complex datasets, the DynaMELD and DynaCOMP models aim to better predict patient outcomes, reducing mortality on the liver transplant waitlist, and using data sciences to offer a more just allocation process for all patients,” said Gary Bader, DSI Associate Director, Research and Software.

The project blends data sciences with health research and modelling as a driver for positive social change, a mandate also at the core of DSI funding ethos through catalyst seed grants.

The team has published part of their work on DynaCOMP at the 2024 Machine Learning for Healthcare conference. Leveraging their DSI seed funding, the researchers were awarded Canadian tri-agency funding and are currently in the process of external validation, using new data sets from different hospital systems and provinces.

“As of February this year, we were awarded a five-year CIHR Grant to expand the scope of DynaMELD to collect data from across Canada,” said Krishnan. “It has really launched a pan-Canadian idea to collect data from Alberta, from Quebec, from BC and the Atlantic provinces, in order to see how different risk scores perform on their data as well.”

But how exactly will DynaMELD and DynaCOMP address issues of inequalities in the current system with respect to liver transplant wait-times and patient selections?

“Let’s say you have 50 individuals who are all waiting for a liver,” said Krishnan. “Doctors need some number to guide them as to who should be ranked first or second or third on the transplant wait list. It’s a number that clinicians sat around the table and came up with about two decades ago.”

“So you have this score that’s been developed, and over the course of time, the score has become less calibrated since the population it was originally designed for has changed. It does not assess risk of mortality as well on women as it does on men, or for patients whose clinical condition deteriorates rapidly. We started rethinking how to calculate this score and, using what we know about AI and machine learning, wondered – what would a new score look like?”

The existing metric, known as the Model for End Stage Liver Disease (MELD)-Na score, can sometimes fail to accurately capture the severity of illness in certain groups, leading to a higher risk of waitlist mortality. Using clinical data from the University Health Network, Krishnan and Bhat used machine learning tools to develop DynaMELD, a more precise and equitable scoring system. The focus of this study included the development of new data science methodology on how changes in patients’ physiological status could be incorporated into risk scores predictive of mortality on the liver transplant waitlist.

“DynaMELD captures not just a patient’s risk of mortality but also their risk of accelerating in terms of likelihood of mortality through changing dynamics over time,” said Krishnan.

“In addition, we wanted to provide clinicians with an early warning system if the subsequent soft tissue graft was not functioning as intended – to create a similar risk score – and that motivated the DynaCOMP part of the project.”

After an individual receives a liver transplant, a common problem that clinicians are often faced with is the likelihood of soft tissue graft failure; DynaCOMP addresses this question.

“We’re very grateful to have received funding from DSI to pursue this project,” Krishnan concluded.

“You need to show evidence that in some sense you put in an effort to de-risk the project before applying for funding and the initial results that we’ve got supported by DSI were very important towards that end.”

By: Cormac Rea

While data science is driving breakthroughs in countless areas, the lack of availability of experimental training data has limited its impact on drug discovery. In particular, there is a need to help data scientists understand experimental drug discovery data, ask the right questions, and decide for themselves on the best answers.  

The Data Sciences Institute (DSI) has awarded the Galvanizing Data Science Applications in Early Stage Drug Discovery proposal as an Emergent Data Science Program, which funds researchers to energize, support, and advance data science.  

The Early Stage Drug Discovery Program will build bridges between data scientists and drug discovery experimentalists – two communities that typically do not speak the same language – by providing training to expose data science trainees to the next frontiers in drug discovery and galvanize a new generation of scientists into a space poised for machine learning-driven transformation. 

The initiative is  led by University of Toronto professors: Matthieu Schapira (Department of Pharmacology and Toxicology, Temerty Faculty of Medicine and the Structural Genomics Consortium); Rachel Harding (Leslie Dan Faculty of Pharmacy, and the Structural Genomics Consortium); Mohamed Moosavi (Department of Chemical Engineering & Applied Science, Faculty of Engineering & Applied Science); Chris Maddison (Department of Computer Science and Department of Statistical Sciences, Faculty of Arts & Science and Vector Institute) and Hui Peng (Department of Chemistry, Faculty of Arts & Science).  

“Recent advances in machine learning (ML) are poised to have a transformative impact along the drug discovery and development trajectory, including finding the best protein target for a given disease, discovering and optimizing drugs and selecting patients most likely to respond to a given treatment,” says lead researcher Matthieu Schapira.  

The Early Stage Drug Discovery program will build bridges between data scientists and drug discovery experimentalists, two communities that typically do not speak the same language.  

Offering quarterly workshops on data science for hit-finding that include interactive sessions and lab visits where data scientists will learn about data generation and experimentalists will learn about data analysis, the program launches on January 31 2025 with the CrossTALK Bootcamp.  

The bootcamp includes workshops to explain the chemical library screening process and associated data challenges in which participants will use their ML models to retrospectively retrieve blinded hits. 

“Supporting emergent areas of data science is a core activity of the Data Sciences Institute that helps to fulfil its mission of bringing people together for collaborative generation and application of new ideas in the data sciences,” says David Lie, DSI Associate Director, Thematic Programming. 

DSI met with Prof. Schapira to learn more about this Emergent Data Science Program:  

From a personal or professional perspective, could you explain what led you and your collaborators to propose this as an emerging data science program to the Data Sciences Institute? 

MS: A challenge for machine learning (ML) in early-stage drug discovery is the lack of publicly accessible, large and consistent data sets to train ML models, but efforts are underway to fill this gap, which will lead to new opportunities for data-science driven drug discovery. A new initiative at The Structural Genomics Consortium (SGC) aims to screen up to 2000 proteins against billions of molecules using two experimental platforms well-established in the pharmaceutical industry: DNA-encoded libraries (DEL) and Affinity Selection Mass Spectrometry (ASMS). A network of AI experts around the world committed to exploiting these data for early-stage drug discovery is rapidly growing at https://aircheck.ai/mainframe. As the SGC, in partnership with our industry partners, is poised to become a leading generator of open-science protein-ligand data, our goal is to ensure that the data science and drug discovery breakthroughs made from our U of T-generated data are not all made elsewhere. Our goal is to position Canada at the forefront of this breakthrough. This grant will enable a pilot project to train the next generation of data scientists at U of T. If successful, we will then expand this program at Universities across Canada.

Our experience with the ML divisions of pharmaceutical companies has revealed that understanding the genesis of the data is critical to elaborate efficient machine learning strategies, and a challenge. Conversely, we believe that it is critical for bench scientists to share a common language with data scientists to better provide guidelines for the reliable interpretation of experimental data.

Our solution is to galvanize Canadian data science trainees around open science data for drug discovery, and pair them with experimentalists. We will organize four bootcamps each year where experimentalists and data scientists team-up and learn together how experimental training datasets are generated, how ML models are built and used to predict bioactive molecules, and how predicted molecules are tested experimentally.   

What are some of the main challenges to bringing together researchers, trainees and students interested in this computational work? 

MS: Most participants will be graduate students and post-docs, though staff are welcome as well… and many PIs say they are keen to attend, though each bootcamp is ~20 hours, which is a real time commitment! I believe pairing experimentalists and data scientists will have a positive impact on the learning curve. Our first bootcamp starts in February, so we’ll see how things go. 

What would you like to see coming out of the CrossTalk bootcamp? 

MS: There is no question that ML will transform the way life sciences are conducted and the speed at which discoveries are made. Canada cannot afford to miss this departing train. U of T is privileged to have a pool of exceptionally talented ML trainees.  

I hope this program will provide some tools for data scientists and experimentalists at U of T and beyond to harness the waves of chemical data that are bound to accelerate early-stage drug discovery. The 2024 Nobel prize in Chemistry highlighted the first steps in this direction.

By: Cormac Rea

Photo: Harry Choi Photograph

At once navigating both the hazards of high-volume car traffic and dilapidated or non-existent urban biking infrastructure, many cyclist commuters in Toronto and Montreal – among other major cities in North America – are well used to running a deadly gauntlet morning and night.

Funded by a Data Sciences Institute catalyst seed grant and co-led by investigators’ Brice Batomen Kuimi (Assistant Professor, Dalla Lana School of Public Health) and Marianne Hatzopolou (Professor, Department of Civil and Mineral Engineering, Faculty of Applied Science and Engineering), Eye on the Street: Using computer vision to capture the determinants of road safety is an innovative project that can contribute to solving the inner-city cycling issue.

The research aims to evaluate several municipal initiatives under Vision-Zero Plans related to road safety and active transportation. Specifically, the project leverages image recognition algorithms to develop a comprehensive database detailing the installation of traffic calming measures across major Canadian cities.

“Getting the DSI funding was very important to building connections, building our team, and getting some initial findings, which has in turn helped us get further funding,” said Brice Batomen. “It’s helped greatly to show credibility of the idea, to bring this work to a bigger stage and to really begin to scale up everything.”

*article reprint from original in Canada Health Technology 

Photos: Canada Health Technology

Participants are seeking employment as data and reporting analysts, data coordinators and technicians.

The Data Sciences Institute (DSI) is a tri-campus, multidisciplinary hub for data science at the University of Toronto (datasciences.utoronto.ca). It facilitates research connections, fosters innovation and enhances teaching and learning in data sciences, including emerging data-driven disciplines. The DSI, with the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada, also offers an intensive, 16-week certificate in data science or machine learning software for people with a university or college degree who have three years or more of work experience. They’re learning programming skills in languages such as Python and SQL. The participants are seeking employment in sectors like healthcare as data analysts, reporting analysts, data coordinators and data technicians.

In this article, we interview leaders at two Canadian companies who are hiring certificate-holders from the Data Sciences Institute. They discuss why they’re working with the DSI, and how hiring Data Science Institute participants will benefit their organizations.

By: Cormac Rea

Photos: Harry Choi Photography

In collaboration with the Dalla Lana School of Public Health, the Data Sciences Institute’s (DSI) Policy Lab recently hosted an enlightening panel discussion at Research Day 2024, entitled Translating Data for Decision Making.  

With a mandate to build capacity and demand across the public sector for data-science insights, through collaborations with ministries, agencies and other policy-oriented groups, the Policy Lab seeks to promote discussion on data science issues and the skillsets needed to address solutions.