Amin Emad

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Membre Académique Associé
Amin Emad
Professeur adjoint, McGill University
Amin Emad

Amin Emad is an Assistant Professor at the Department of Electrical and Computer Engineering at McGill University and is an Associate Member at Mila. He is also affiliated with the McGill initiative in Computational Medicine (MiCM) and the Quantitative Life Sciences (QLS) program at McGill University as well as with the National Center for Supercomputing Applications (NCSA) at the University of Illinois (UIUC). Before joining McGill, he was a Postdoctoral Research Associate at the NIH KnowEnG Center of Excellence in Big Data Computing associated with the Department of Computer Science and the Institute for Genomic Biology (IGB) at 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 in 2007, all in Electrical and Computer Engineering.

Dr. Emad’s research is focused on the application of machine learning (e.g. deep learning, machine learning on graphs, etc.) in bioinformatics and computational biology. Particularly, he and his team develop novel computational tools to address various challenges in biology and medicine. These include problems such as prediction of drug response using ‘omic’ molecular profiles of patients, identification of biomarkers of drug sensitivity (e.g. in cancer, in kidney diseases, in chronic lung diseases, etc.), identification of disease subtypes to improve patient diagnosis, and deciphering the gene regulatory mechanisms associated with a phenotype. His other areas of interest include interpretable machine learning, machine learning for communication systems, graph mining, sparse inverse problems, and group testing.



A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.
Jun Ding, David Earl Hostallero, Mohamed Reda El Khili, Gregory Joseph Fonseca, Simon Milette, Nuzha Noorah, Myriam Guay-Belzile, Jonathan Spicer, Noriko Daneshtalab, Martin Sirois, Karine Tremblay, Amin Emad and Simon Rousseau
PLOS Computational Biology
(2021-03-08)[Also on medRxiv (2020-07-02)]


Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study
Amin Emad and Saurabh Sinha
NPJ systems biology and applications


Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets
Chen Su, Simon Rousseau and Amin Emad


A recursive framework for predicting the time-course of drug sensitivity.
Cheng Qian, Amin Emad and Nicholas D Sidiropoulos
Scientific Reports


Distinct miRNA Profile of Cellular and Extracellular Vesicles Released from Chicken Tracheal Cells Following Avian Influenza Virus Infection.
Kelsey O'Dowd, Mehdi Emam, Mohamed Reda El Khili, Amin Emad, Eveline M Ibeagha-Awemu, Carl A Gagnon and Neda Barjesteh


Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature.
Amin Emad, Tania Ray, Tor W. Jensen, Meera Parat, Rachael Natrajan, Saurabh Sinha and Partha S. Ray
Breast Cancer Research

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