Institution
Université de Montréal
Education•Montreal, 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.
Topics: Population, Poison control, Health care, Receptor, Prostate cancer
Papers published on a yearly basis
Papers
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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
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Stephen Sawcer1, Garrett Hellenthal2, Matti Pirinen2, Chris C. A. Spencer2 +262 more•Institutions (67)
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
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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
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University of Kiel1, Cedars-Sinai Medical Center2, Wellcome Trust Sanger Institute3, University of Pennsylvania4, QIMR Berghofer Medical Research Institute5, Peninsula College of Medicine and Dentistry6, University of Edinburgh7, University of Cambridge8, University of Otago9, University of Washington10, University of Groningen11, University of Liège12, Harvard University13, Casa Sollievo della Sofferenza14, King's College London15, University of Chicago16, Yale University17, Johns Hopkins University18, Ludwig Maximilian University of Munich19, Charité20, McGill University21, Lille University of Science and Technology22, Cincinnati Children's Hospital Medical Center23, Ghent University24, Torbay Hospital25, Mater Health Services26, Université libre de Bruxelles27, RWTH Aachen University28, University of Utah29, Örebro University30, Leiden University31, University of Paris32, Technion – Israel Institute of Technology33, University of Western Australia34, Tel Aviv University35, University of Dundee36, University of Manchester37, University of Pittsburgh38, Royal Hospital for Sick Children39, Katholieke Universiteit Leuven40, Guy's and St Thomas' NHS Foundation Trust41, University of Bern42, University of Toronto43, University of Amsterdam44, Karolinska Institutet45, University of Zurich46, Université de Montréal47, Emory University48, Newcastle University49
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
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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
Name | H-index | Papers | Citations |
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Yoshua Bengio | 202 | 1033 | 420313 |
Alan C. Evans | 183 | 866 | 134642 |
Richard H. Friend | 169 | 1182 | 140032 |
Anders Björklund | 165 | 769 | 84268 |
Charles N. Serhan | 158 | 728 | 84810 |
Fernando Rivadeneira | 146 | 628 | 86582 |
C. Dallapiccola | 136 | 1717 | 101947 |
Michael J. Meaney | 136 | 604 | 81128 |
Claude Leroy | 135 | 1170 | 88604 |
Georges Azuelos | 134 | 1294 | 90690 |
Phillip Gutierrez | 133 | 1391 | 96205 |
Danny Miller | 133 | 512 | 71238 |
Henry T. Lynch | 133 | 925 | 86270 |
Stanley Nattel | 132 | 778 | 65700 |
Lucie Gauthier | 132 | 679 | 64794 |