Portrait de Amin Emad

Amin Emad

Membre académique associé
emadamin@mila.quebec
Professeur agrégé, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Apprentissage profond
Biologie computationnelle
Découverte de médicaments
Épigénomique
Généralisation hors distribution (OOD)
Génomique
Microbiome
Modèles génératifs
Modélisation moléculaire
Science des réseaux
Transcriptomique
Site web

Biographie

Amin Emad est directeur du COMBINE Lab (Computational Biology and Artificial Intelligence). Il est professeur agrégé au Département de génie électrique et informatique de l'Université McGill et membre académique associé à Mila – Institut québécois d'intelligence artificielle.

Il est églement affilié à l'Institut du cancer Rosalind et Morris Goodman, à l'Initiative de McGill en médecine computationnelle (MiCM), au programme des Sciences quantitatives de la vie et aux Laboratoires Meakins-Christie de l'Université McGill.

Avant de se joindre à McGill, il a été associé de recherche postdoctorale au centre d'excellence KnowEnG des National Institutes of Health (NIH) en informatique des mégadonnées, un centre lié au Département d'informatique et à l'Institut de biologie génomique (IGB) de l'Université de l'Illinois à Urbana-Champaign (UIUC). Il a obtenu un doctorat de l'UIUC.

Étudiants actuels

Publications

RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks
Joseph Szymborski
Amin Emad
Online-only supplementary data is available at the journal’s website.
2021-12-31
Bioinformatics (publié)
doi.org
Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2
Su Chen
Simon Rousseau
Amin Emad
Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-C… (voir plus)oV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.
2021-12-13
Scientific Reports (publié)
doi.org
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
Neda Barjesteh
Innate responses provide the first line of defense against viral infections, including the influenza virus at mucosal surfaces. Communicatio… (voir plus)n and interaction between different host cells at the early stage of viral infections determine the quality and magnitude of immune responses against the invading virus. The release of membrane-encapsulated extracellular vesicles (EVs), from host cells, is defined as a refined system of cell-to-cell communication. EVs contain a diverse array of biomolecules, including microRNAs (miRNAs). We hypothesized that the activation of the tracheal cells with different stimuli impacts the cellular and EV miRNA profiles. Chicken tracheal rings were stimulated with polyI:C and LPS from Escherichia coli 026:B6 or infected with low pathogenic avian influenza virus H4N6. Subsequently, miRNAs were isolated from chicken tracheal cells or from EVs released from chicken tracheal cells. Differentially expressed (DE) miRNAs were identified in treated groups when compared to the control group. Our results demonstrated that there were 67 up-regulated miRNAs, 157 down-regulated miRNAs across all cellular and EV samples. In the next step, several genes or pathways targeted by DE miRNAs were predicted. Overall, this study presented a global miRNA expression profile in chicken tracheas in response to avian influenza viruses (AIV) and toll-like receptor (TLR) ligands. The results presented predicted the possible roles of some DE miRNAs in the induction of antiviral responses. The DE candidate miRNAs, including miR-146a, miR-146b, miR-205a, miR-205b and miR-449, can be investigated further for functional validation studies and to be used as novel prophylactic and therapeutic targets in tailoring or enhancing antiviral responses against AIV.
2020-08-04
Vaccines (publié)
doi.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-… (voir plus)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.
2015-08-14
IEEE Transactions on Signal Processing (publié)
doi.org
arxiv.org