Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Ahmad Pesaranghader"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from EHR data has been hindered by the sparse
Externí odkaz:
https://doaj.org/article/f777d40a70544702b634461c65c9b2c2
Autor:
Fatima Mostefai, Isabel Gamache, Arnaud N'Guessan, Justin Pelletier, Jessie Huang, Carmen Lia Murall, Ahmad Pesaranghader, Vanda Gaonac'h-Lovejoy, David J. Hamelin, Raphaël Poujol, Jean-Christophe Grenier, Martin Smith, Etienne Caron, Morgan Craig, Guy Wolf, Smita Krishnaswamy, B. Jesse Shapiro, Julie G. Hussin
Publikováno v:
Frontiers in Medicine, Vol 9 (2022)
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data.
Externí odkaz:
https://doaj.org/article/15d0f0d674344af6963c8d1f5b18412c
Autor:
Walid Ben Ali, Ahmad Pesaranghader, Robert Avram, Pavel Overtchouk, Nils Perrin, Stéphane Laffite, Raymond Cartier, Reda Ibrahim, Thomas Modine, Julie G. Hussin
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 8 (2021)
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical
Externí odkaz:
https://doaj.org/article/9e629fc59b5b468fa204c763b67423ff
Autor:
Ahmad Pesaranghader, Stan Matwin, Marina Sokolova, Jean-Christophe Grenier, Robert G Beiko, Julie Hussin
Publikováno v:
Bioinformatics. 38:3051-3061
Motivation There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein–protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application
Autor:
Fatima Mostefai, Isabel Gamache, Arnaud N'Guessan, Justin Pelletier, Jessie Huang, Carmen Lia Murall, Ahmad Pesaranghader, Vanda Gaonac'h-Lovejoy, David J. Hamelin, Raphaël Poujol, Jean-Christophe Grenier, Martin Smith, Etienne Caron, Morgan Craig, Guy Wolf, Smita Krishnaswamy, B. Jesse Shapiro, Julie G. Hussin
Publikováno v:
Frontiers in medicine. 9
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data.
Autor:
B. Jesse Shapiro, Etienne Caron, David Hamelin, Isabel Gamache, Arnaud N’Guessan, Morgan Craig, Jean-Christophe Grenier, Smita Krishnaswamy, Julie Hussin, Jessie Huang, Fatima Mostefai, Guy Wolf, Ahmad Pesaranghader, Justin Pelletier, Martin A. Smith, Raphaël Poujol, Carmen Lía Murall
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6ce4206928cddbf2f1c6dda4014e3d2
https://doi.org/10.1101/2021.09.28.462270
https://doi.org/10.1101/2021.09.28.462270
We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d0bab6c72c23d25dc079903822edacd
https://doi.org/10.1101/2021.08.13.456305
https://doi.org/10.1101/2021.08.13.456305
Publikováno v:
Deep Generative Models, and Data Augmentation, Labelling, and Imperfections ISBN: 9783030882099
DGM4MICCAI/DALI@MICCAI
DGM4MICCAI/DALI@MICCAI
Diversity in data is critical for the successful training of deep learning models. Leveraged by a recurrent generative adversarial network, we propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes (\({\ge }224\times 224\ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84745fe56ee21b74bce0f29f3f427561
https://doi.org/10.1007/978-3-030-88210-5_6
https://doi.org/10.1007/978-3-030-88210-5_6
Autor:
Thomas Fevens, Lisa Di Jorio, Francis Dutil, Mohammad Havaei, Ahmad Pesaranghader, Qicheng Lao
Publikováno v:
ICCV
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color, composition, etc.), and the style, which is usually not well describe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11b455cb3e779415da797b574e83ddbb
http://arxiv.org/abs/1908.05324
http://arxiv.org/abs/1908.05324
Publikováno v:
J Am Med Inform Assoc
ObjectiveIn biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3dad8f8aead4f9a93f96d39edb6df770
https://europepmc.org/articles/PMC7787358/
https://europepmc.org/articles/PMC7787358/