Portrait of Amin Emad

Amin Emad

Associate Academic Member
Assistant Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Causality
Computational Biology
Deep Learning
Generative Models
Graph Neural Networks
Learning on Graphs
Molecular Modeling
Multimodal Learning
Probabilistic Models
Representation Learning

Biography

Amin Emad is an assistant professor in the Department of Electrical and Computer Engineering at McGill University and an associate academic member of Mila – Quebec Artificial Intelligence Institute.

He is affiliated with McGill’s Rosalind and Morris Goodman Cancer Institute, the McGill initiative in Computational Medicine (MiCM), McGill’s Quantitative Life Sciences (QLS) program, and the Meakins-Christie Laboratories at the McGill University Hospital Centre.

Before joining McGill, Emad was a postdoctoral research associate at the NIH-funded KnowEnG – A Center of Excellence in Big Data Computing, which is associated with the Department of Computer Science and the Institute for Genomic Biology at the University of Illinois at Urbana-Champaign (UIUC). He received his PhD from UIUC in 2015, his MSc from the University of Alberta in 2009, and his BSc from Sharif University of Technology (Tehran) in 2007. Emad’s research lies at the intersection of AI and computational biology.

Current Students

Nicolas Bolouri
Undergraduate - McGill University
Github
Google Scholar
Shayan Hajhashemi
PhD - McGill University
Github
Orsolya Lapohos
PhD - McGill University
Github
William Ma
Master's Research - McGill University
Github
Ali Saberi
PhD - McGill University
Cedrique Shum-Tim
PhD - McGill University
Github
Kiri Stern
PhD - McGill University
Chen Su
PhD - McGill University
Justin Szeto
PhD - McGill University
Github
Joseph Szymborski
PhD - McGill University
Website
Github

Publications

Validation of ANG-1 and P-SEL as biomarkers of post-COVID-19 conditions using data from the Biobanque québécoise de la COVID-19 (BQC-19)
Eric Yamga
Antoine Soulé
Alain Piché
Amin Emad
Madeleine Durand
Simon Rousseau
2023-10-24
Clinical Proteomics (published)
doi.org
Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Yihui Li
David Earl Hostallero
Amin Emad
Motivation: Recent advances in deep learning model development have enabled more accurate prediction of drug response in cancer. However, th… (see more)e black-box nature of these models still remains a hurdle in their adoption for precision cancer medicine. Recent efforts have focused on making these models interpretable by incorporating signaling pathway information in model architecture. While these models improve interpretability, it is unclear whether this higher interpretability comes at the cost of less accurate predictions, or a prediction improvement can also be obtained. Results: In this study, we comprehensively and systematically assessed four state-of-the-art interpretable models developed for drug response prediction to answer this question using three pathway collections. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. Moreover, in most evaluation setups the best performance is achieved using a simple black-box model. In addition, replacing the signaling pathways with randomly generated pathways shows a comparable performance for the majority of these interpretable models. Our results suggest that new interpretable models are necessary to improve the drug response prediction performance. In addition, the current study provides different baseline models and evaluation setups necessary for such new models to demonstrate their superior prediction performance. Availability and Implementation: Implementation of all methods are provided in https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP. Generated uniform datasets are in https://zenodo.org/record/7101665#.YzS79HbMKUk. Contact: amin.emad@mcgill.ca Supplementary Information: Online-only supplementary data is available at the journal’s website.
2023-06-16
Bioinformatics (published)
doi.org
MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores
Mohamed Reda El Khili
Safyan Aman Memon
Amin Emad
Motivation Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improv… (see more)e outcome. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values. Results Here, we developed MARSY, a deep learning multi-task model that incorporates information on gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug-pairs, as well as the drug-pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine learning models. Using MARSY, we then predicted the synergy scores of 133,722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. Availability and Implementation An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY. Contact amin.emad@mcgill.ca Supplementary Information Online-only supplementary data is available at the journal’s website.
2023-04-06
Bioinformatics (published)
doi.org
Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies
Chen-Fang Chung
Joan Papillon
José R. Navarro-Betancourt
Julie Guillemette
Ameya Bhope
Amin Emad
Andrey V. Cybulsky
2023-03-13
Frontiers in Medicine (published)
doi.org
Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL
David Earl Hostallero
Lixuan Wei
Liewei Wang
Junmei Cairns
Amin Emad
2023-02-01
Genomics, Proteomics & Bioinformatics (published)
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
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
2023-01-01
(published)
www.semanticscholar.org
Poisson Group Testing: A Probabilistic Model for Boolean Compressed Sensing
Amin Emad
Olgica Milenkovic
We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-… (see more)truncated Poisson distribution. The Poisson model has a number of new applications, including dynamic testing with diminishing relative rates of defectives. We consider both nonadaptive and semi-adaptive identification methods. For nonadaptive methods, we derive a lower bound on the number of tests required to identify the defectives with a probability of error that asymptotically converges to zero; in addition, we propose test matrix constructions for which the number of tests closely matches the lower bound. For semiadaptive methods, we describe a lower bound on the expected number of tests required to identify the defectives with zero error probability. In addition, we propose a stage-wise reconstruction algorithm for which the expected number of tests is only a constant factor away from the lower bound. The methods rely only on an estimate of the average number of defectives, rather than on the individual probabilities of subjects being defective.
2015-08-15
IEEE Transactions on Signal Processing (published)
doi.org
arxiv.org
Poisson Group Testing: A Probabilistic Model for Boolean Compressed Sensing
Amin Emad
Olgica Milenkovic
We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-… (see more)truncated Poisson distribution. The Poisson model has a number of new applications, including dynamic testing with diminishing relative rates of defectives. We consider both nonadaptive and semi-adaptive identification methods. For nonadaptive methods, we derive a lower bound on the number of tests required to identify the defectives with a probability of error that asymptotically converges to zero; in addition, we propose test matrix constructions for which the number of tests closely matches the lower bound. For semiadaptive methods, we describe a lower bound on the expected number of tests required to identify the defectives with zero error probability. In addition, we propose a stage-wise reconstruction algorithm for which the expected number of tests is only a constant factor away from the lower bound. The methods rely only on an estimate of the average number of defectives, rather than on the individual probabilities of subjects being defective.
2014-10-20
ArXiv (preprint)
doi.org
arxiv.org