COVID-19

Mila COVID-19 Related Projects

In light of the challenges that COVID-19 presents to our society, Mila is bringing its machine learning expertise to the scientific community together with its partners across different disciplines to help find potential solutions.

To join the effort, you are encouraged to reachout :

Read the press release

Join the AI Against COVID-19 Slack Channel

Get involved

Submit a project

AI Against COVID-19 Canada

AI Against COVID-19 Canada is a special task force led by a community of researchers from CIFARMila – Quebec Artificial Research Institute, the Vector Institute and Amii.

The team is aiming to map and coordinate AI projects in Canada that can contribute to solve the COVID-19 outbreak and limit its impact on society.

Visit the AI Against COVID-19 Canada website

Peer-to-peer AI-based tracing of COVID-19

Contact: Yoshua Bengio (Mila/UdeM)

Description: The objective is to make confinement more efficient by being selective, i.e., allowing the people at lower risk to be under less constraints while putting stronger isolation pressure on those at high risk of being contagious. The project is going at full speed with two dozen people involved to develop a tracing app for Canada and Quebec, with a ML component to predict a person’s probability of having the disease based on contact history and medical information. The project has a strong focus on privacy (aiming at peer-to-peer communication to avoid a central repository of individuals’ movements) and relies on worldwide open-source efforts going on to develop the required building blocks. 

Read Yoshua Bengio's article

Data-Against-COVID-19

Contact: Fred Hamprecht (Heidelberg University), Nasim Rahaman (Mila/Tuebingen University), Florian Jug (MPI), Oliver Stegle (EMBL).

Description: Collaborative Space for Enabling and Accelerating Collaborations between Medical Professionals and Data Scientists.

Visit the Data-Against-COVID-19 website

COVID-19 drug development, multi-omic profiling and viral evolution studies

Contact: Julie Hussin (MHI; UdeM), Marie-Pierre Dubé (MHI, UdeM), Martin Smith (CHUSJ; UdeM), Guy Wolf (Mila; UdeM), Smita Krishnaswamy (Yale), Irina Rish (Mila; UdeM)

Description: This family of inter-connected projects focuses on medical data collection (omics – genomic, proteomic, transcriptomic, and clinical data), combined with statistical machine learning analysis, in order to achieve a better mechanistic understanding of the COVID-19 disease progression that can facilitate new drug development and/or repurposing existing drugs, via identification of binding targets for antiviral agents and potential vaccines, as well as for more personalized patient treatment due to more accurate assessment of disease progression and risk level. We are interested in viral evolution and viral-host interactions of SARS-CoV-2 strains that may affect humans differently depending on their ancestry, sex, age, co-morbidities, current medication and personal genetic profile. Such interactions may be key for understanding how the epidemic is unraveling and to eventually develop personalized treatments.

Project 1: Viral evolution profiling

Description: Since the beginning of the SARS-CoV-2 outbreak, the virus causing COVID-19 is and has been evolving from its original RNA sequence from a bat viral strain. Using deep learning models and genomic sequences of the SARS-CoV-2 virus from patients, we will aim to detect interaction effects within the viral genomic sequences. In particular, we will apply artificial neural networks to time-sampled whole-genome datasets for feature learning to analyze allelic interactions. Second, neural network approaches can be trained from the large amount of sequences available from diverse geographic regions to predict the possible point mutations that appear on RNA sequences based on the other nucleotides in the sequence. These approaches may reveal mid-range interactions across the viral genome and mutations that arise on specific proteins in connection to treatment response and disease severity.

Project 2: Dynamics of the immune response in young COVID-19 patients

Description: Why do children and young adults rarely present severe symptoms to SARS-CoV-2 infection? What causes some young patients to have severe symptoms? CHU Ste-Justine, the largest mother and child centre in Canada, will collect clinical samples and data on young patients who are admitted to the ICU for COVID-19 to uncover the molecular and clinical features of these rare immune responses. We will generate, aggregate and interrogate multi-omic data, including patient genomes, virus genomes, and single molecule blood transcriptomes, with comprehensive clinical data to identify common features of severe pediatric COVID-19 cases. These features will then guide the design of functional studies to dissect the dynamics of the immune response to SARS-CoV-2 infection, with the potential to discover prognostic biomarkers.

Machine learning for therapeutics discovery

Deep Reinforcement Learning for Discovering Novel COVID19 Antivirals 

Contact: Yoshua Bengio (UdeM), Jian Tang (HEC Montréal), 

Description: The teams of Yoshua Bengio (UdeM), Jian Tang (HEC Montréal) and graduate student Maksym Korablyov (UdeM), have developed a deep reinforcement learning system, LambdaZero, which can quickly evaluate billions of candidate molecules. The approach can gradually modify the molecular structure by adding or removing building blocks in order to converge toward new molecular structures that can bind a target protein. The Mila team is looking for collaborators with expertise in deep reinforcement learning and in drug discovery to help with their research.

Visit the project's web page

Data-efficient deep learning to better model immune response

Contact: Yoshua Bengio (UdeM/Mila), Sébastien Giguère (InVivo AI), Julie Hussin (MHI; UdeM), Natasha Dudek (McGill/Mila), Matthew Scicluna (UdeM/Mila)

Description: In collaboration with machine learning research at Mila, researchers in Bioinformatics, Immunology, and Vaccine design, InVivo AI is building an open-source platform leveraging the latest AI technologies to model pathways in the immune system in order to better predict immune response. The team is working on modeling proteasomal cleavage, TAP transport, antigen binding to MHC, antigen processing to the cell surface and recognition of MHC-antigen complexes by T-Cells and B-cells, with the goal of providing insight into immunogenic regions of the virus. 

More specifically, we work on AI approaches that can contribute to the process of vaccine design in the following ways:

  1. Antigen-MHC prediction: Major Histocompatibility Complexes (MHC) are proteins responsible for safeguarding cells and detecting intruding materials such as viral proteins. Each person has a large repertoire of MHC molecules, each able to recognize specific antigens, and each of us has a distinct repertoire. Predicting which part of the viral protein (the antigens) can be recognized by which MHC can be formulated as a supervised learning problem where the input is composed of the sequence of a MHC (ex. “MEPSLLSLFVLGVLT…”) and an antigen (ex. “TAVVAGAVIL”), while the output is the affinity between that antigen-MHC pair. 
  2. Antigens processing: Another example would be predicting which of these positive pairs get processed to the surface of the cell. Members of our team have already published on these two problems, the resulting tool has won international recognition for its accuracy, and is hosted on the leading database in immunology (http://tools.iedb.org/mhcnp/). 

The team is working on modeling the complete antigen processing pathway: proteasomal cleavage, TAP transport, antigen binding to MHC, antigen processing to the cell surface and recognition of MHC-antigen complexes by T-Cells and B-cells, with the goal of providing insight into immunogenic regions of the viruses.  We are seeking collaborators across all disciplines to work toward the goal of designing better vaccines. Get in touch with us on the #ai-for-vaccines channel of our slack or send a note to Sébastien Giguère (sebastien@invivoai.com).

Mapping the Landscape of AI + COVID-19

Contact: Sasha Luccioni (UdeM/Mila)

Description:  Sasha Luccioni (postdoctoral researcher at Mila) and her team are working with researchers from UN Global Pulse to map the landscape of current and proposed research papers that use AI to counter the COVID-19 pandemic. The article, published on March 25th, covers the many facets of the crisis, including molecular modeling, epidemiology and diagnosis, in order to pinpoint the most impactful ways in which AI can be used and the approaches that are being explored.

Read the article

Analyzing the Online Information Space around COVID-19

Contact:  Reihaneh Rabbany (McGill/Mila)

Description: This project aims to study the information posted online surrounding this global pandemic by looking at the mentions of COVID-19 on Twitter. The goal is to discover temporal and spatial trends (to get the pulse and also serve as proxy data), summarize the content (to help the users better understand what is happening) and detect potential polluting groups (to help fight the correlated infodemic). This is important given that the current flattening of the curve models assume some specific degree of adherence from the public and this is affected by the information they receive. The scale of this infodemic has overwhelmed the content moderators for these platforms who also have to work from home, which signifies the need for AI solutions. 

COVID-19 Image Data Collection

Contact: Joseph Paul Cohen (Mila/UdeM)

Description: In the context of a COVID-19 pandemic, is it crucial to streamline diagnosis. Data is the first step to developing any diagnostic tool or treatment. While there exist large public datasets of more typical chest X-rays, there is no collection of COVID-19 chest X-rays or CT scans designed to be used for computational analysis. Our team believes that this open database can dramatically improve identification of COVID-19. Notably, this would provide essential data to train and test a Deep Learning based system such as Chester, likely using some form of transfer learning. These tools could be developed to identify COVID-19 characteristics as compared to other types of pneumonia or in order to predict survival. The database is here:  and a paper with more information about the project is here. We welcome contributions to the dataset as well as contributors.

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Mila goes virtual

Starting March 16, 2020, Mila shifts its activities to virtual platforms in order to minimize COVID-19 diffusion.

Read more