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William Ma

william.ma@mila.quebec
Master's Research - McGill University
Supervisor
Amin Emad
Research Topics
Causality
Computational Biology
Data Mining
Deep Learning
Graph Neural Networks
Knowledge Graphs
Learning on Graphs
Medical Machine Learning
Multimodal Learning
Recurrent Neural Networks
Representation Learning
GitHub

Publications

Unbiased characterization of COVID-19 endotypes leads to prognostication of high-risk individuals using routine blood tests
William Ma
Antoine Soulé
Catherine Allard
Madeleine Durand
Karine Tremblay
Simon Rousseau
Amin Emad
Unsupervised proteomic analysis identified biologically coherent endotypes that advance understanding of acute lung injury in COVID‑19 and… (see more) support improved diagnostic and prognostic strategies.
2026-03-13
Communications Medicine (published)
doi.org
Molecular pathology of acute respiratory distress syndrome, mechanical ventilation and abnormal coagulation in severe COVID-19
Antoine Soulé
William Ma
Katelyn Yixiu Liu
Catherine Allard
Salman Qureshi
Karine Tremblay
Amin Emad
Simon Rousseau
Systemic inflammation in critically ill patients can lead to serious consequences such as acute respiratory distress syndrome (ARDS), a cond… (see more)ition characterized by the presence of lung inflammation, edema, and impaired gas exchange, associated with poor survival. Understanding molecular pathobiology is essential to improve critical care of these patients. To this end, we use multimodal profiles of SARS-CoV-2 infected hospitalized participants to the Biobanque Québécoise de la COVID-19 (BQC-19) to characterize endophenotypes associated with different degrees of disease severity. Proteomic, metabolomic, and genomic characterization supported a role for neutrophil-associated procoagulant activity in severe COVID-19 ARDS that is inversely correlated with sphinghosine-1 phosphate plasma levels. Fibroblast Growth Factor Receptor (FGFR) and SH2-containing transforming protein 4 (SHC4) signaling were identified as molecular features associated with endophenotype 6 (EP6). Mechanical ventilation in EP6 was associated with alterations in lipoprotein metabolism. These findings help define the molecular mechanisms related to specific severe outcomes, that can be used to identify early unfavorable clinical trajectories and treatable traits to improve the survival of critically ill patients.
2023-03-09
medRxiv (preprint)
doi.org
A circulating proteome-informed prognostic model of COVID-19 disease activity that relies on 1 routinely available clinical laboratories 2
William Ma
Antoine Soulé
Karine Tremblay
Simon Rousseau
Amin Emad
Abstract
2022-12-31
(published)
www.semanticscholar.org
A circulating proteome-informed prognostic model of COVID-19 disease activity that relies on routinely available clinical laboratories
William Ma
Antoine Soulé
Karine Tremblay
Simon Rousseau
Amin Emad
A minority of people infected with SARS-CoV-2 will develop severe COVID-19 disease. To help physicians predict who is more likely to require… (see more) admission to ICU, we conducted an unsupervised stratification of the circulating proteome that identified six endophenotypes (EPs) among 731 SARS-CoV-2 PCR-positive hospitalized participants in the Biobanque Québécoise de la COVID-19, with varying degrees of disease severity and times to intensive care unit (ICU) admission. One endophenotype, EP6, was associated with a greater proportion of ICU admission, ventilation support, acute respiratory distress syndrome (ARDS) and death. Clinical features of EP6 included increased levels of C-reactive protein, D-dimers, interleukin-6, ferritin, soluble fms-like tyrosine kinase-1, elevated neutrophils, and depleted lymphocytes, whereas another endophenotype (EP5) was associated with cardiovascular complications, congruent with elevated blood biomarkers of cardiovascular disease like N-terminal pro B-type natriuretic peptide (NT-proBNP), Growth Differentiation Factor-15 (GDF-15), and Troponin T. Importantly, a prognostic model solely based on clinical laboratory measurements was developed and validated on 903 patients that generalizes the EPs to new patients recruited across all pandemic waves (2020-2022) and create new opportunities for automated identification of high-risk groups in the clinic. Thus, this novel way to address pathogenesis that leverages detailed phenotypic information but relies on routinely available information in the clinic to favor translation may find applications in other diseases beyond COVID-19.
2022-11-03
medRxiv (preprint)
doi.org