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Institution

Université de Montréal

EducationMontreal, Quebec, Canada
About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.


Papers
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Journal ArticleDOI
TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.

2,538 citations

Journal ArticleDOI
10 Aug 2011-Nature
TL;DR: In this article, a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, they have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci.
Abstract: Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.

2,511 citations

Posted Content
TL;DR: Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases.
Abstract: In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

2,510 citations

Journal ArticleDOI
Andre Franke1, Dermot P.B. McGovern2, Jeffrey C. Barrett3, Kai Wang4, Graham L. Radford-Smith5, Tariq Ahmad6, Charlie W. Lees7, Tobias Balschun1, James Lee8, Rebecca L. Roberts9, Carl A. Anderson3, Joshua C. Bis10, Suzanne Bumpstead3, David Ellinghaus1, Eleonora M. Festen11, Michel Georges12, Todd Green13, Talin Haritunians2, Luke Jostins3, Anna Latiano14, Christopher G. Mathew15, Grant W. Montgomery5, Natalie J. Prescott15, Soumya Raychaudhuri13, Jerome I. Rotter2, Philip Schumm16, Yashoda Sharma17, Lisa A. Simms5, Kent D. Taylor2, David C. Whiteman5, Cisca Wijmenga11, Robert N. Baldassano4, Murray L. Barclay9, Theodore M. Bayless18, Stephan Brand19, Carsten Büning20, Albert Cohen21, Jean Frederick Colombel22, Mario Cottone, Laura Stronati, Ted Denson23, Martine De Vos24, Renata D'Incà, Marla Dubinsky2, Cathryn Edwards25, Timothy H. Florin26, Denis Franchimont27, Richard B. Gearry9, Jürgen Glas22, Jürgen Glas28, Jürgen Glas19, André Van Gossum27, Stephen L. Guthery29, Jonas Halfvarson30, Hein W. Verspaget31, Jean-Pierre Hugot32, Amir Karban33, Debby Laukens24, Ian C. Lawrance34, Marc Lémann32, Arie Levine35, Cécile Libioulle12, Edouard Louis12, Craig Mowat36, William G. Newman37, Julián Panés, Anne M. Phillips36, Deborah D. Proctor17, Miguel Regueiro38, Richard K Russell39, Paul Rutgeerts40, Jeremy D. Sanderson41, Miquel Sans, Frank Seibold42, A. Hillary Steinhart43, Pieter C. F. Stokkers44, Leif Törkvist45, Gerd A. Kullak-Ublick46, David C. Wilson7, Thomas D. Walters43, Stephan R. Targan2, Steven R. Brant18, John D. Rioux47, Mauro D'Amato45, Rinse K. Weersma11, Subra Kugathasan48, Anne M. Griffiths43, John C. Mansfield49, Severine Vermeire40, Richard H. Duerr38, Mark S. Silverberg43, Jack Satsangi7, Stefan Schreiber1, Judy H. Cho17, Vito Annese14, Hakon Hakonarson4, Mark J. Daly13, Miles Parkes8 
TL;DR: A meta-analysis of six Crohn's disease genome-wide association studies and a series of in silico analyses highlighted particular genes within these loci implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP.
Abstract: We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.

2,482 citations

Journal ArticleDOI
TL;DR: A generative adversarial networks algorithm designed to solve the generative modeling problem and its applications in medicine, education and robotics are studied.
Abstract: Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.

2,447 citations


Authors

Showing all 45957 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Alan C. Evans183866134642
Richard H. Friend1691182140032
Anders Björklund16576984268
Charles N. Serhan15872884810
Fernando Rivadeneira14662886582
C. Dallapiccola1361717101947
Michael J. Meaney13660481128
Claude Leroy135117088604
Georges Azuelos134129490690
Phillip Gutierrez133139196205
Danny Miller13351271238
Henry T. Lynch13392586270
Stanley Nattel13277865700
Lucie Gauthier13267964794
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023118
2022485
20216,077
20205,753
20195,212
20184,696