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Ahmad Pesaranghader

Alumni

Publications

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model
Yuesong Zou
Ahmad Pesaranghader
Ziyang Song
Aman Verma
David Buckeridge
Yue Li
2022-10-25
Scientific Reports (publié)
doi.org
Modeling electronic health record data using a knowledge-graph-embedded topic model
Yuesong Zou
Ahmad Pesaranghader
Aman Verma
David Buckeridge
Yue Li
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (voir plus)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.
2022-06-03
ArXiv (prépublication)
doi.org
arxiv.org
Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages
Fatima Mostefai
Isabel Gamache
Arnaud N’Guessan
Justin Pelletier
Jessie Huang
Carmen Lia Murall
Ahmad Pesaranghader
Vanda Gaonac'h-Lovejoy
David J. Hamelin
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
Guy Wolf
Smita Krishnaswamy
B. Jesse Shapiro
Julie Hussin
2022-02-21
Frontiers in Medicine (publié)
doi.org
Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages
Fatima Mostefai
I. Gamache
Arnaud N’Guessan
Justin Pelletier
Jessie Huang
Carmen Lia Murall
Ahmad Pesaranghader
Vanda Gaonac'h-Lovejoy
David J. Hamelin
Raphael Poujol
Jean-Christophe Grenier
Martin W. Smith
Étienne Caron
Morgan Craig
Guy Wolf
Smita Krishnaswamy
B. Jesse Shapiro
Julie Hussin
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.
2022-02-21
Frontiers in Medicine (published)
doi.org
Data-driven approaches for genetic characterization of SARS-CoV-2 lineages
Fatima Mostefai
Isabel Gamache
Jessie Huang
Arnaud N’Guessan
Justin Pelletier
Ahmad Pesaranghader
David J. Hamelin
Carmen Lia Murall
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
Jesse Shapiro
Guy Wolf
Smita Krishnaswamy
Julie Hussin
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.
2021-09-29
bioRxiv (prépublication)
doi.org