Institution
University of Toronto
Education•Toronto, Ontario, Canada•
About: University of Toronto is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 126067 authors who have published 294940 publications receiving 13536856 citations. The organization is also known as: UToronto & U of T.
Papers published on a yearly basis
Papers
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12 Dec 2011TL;DR: This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks and revisits several common regularisers from a variational perspective.
Abstract: Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus.
1,341 citations
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TL;DR: Smad7 is defined as an adaptor in an E3 ubiquitin-ligase complex that targets the TGF beta receptor for degradation, and mutants that interfere with recruitment of Smurf2 to the receptors are compromised in their inhibitory activity.
1,340 citations
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McMaster University1, University of Toronto2, Dalhousie University3, Pennsylvania State University4, Nationwide Children's Hospital5, University of Iowa6, University of Miami7, University of South Carolina8, University of Paris9, Pasteur Institute10, University of Gothenburg11, Icahn School of Medicine at Mount Sinai12, Stanford University13, Vanderbilt University14, Johns Hopkins University15, University of North Carolina at Chapel Hill16, University of California, Los Angeles17, University of Pennsylvania18, Washington University in St. Louis19, University of Chicago20, Harvard University21, Emory University22, George Washington University23, Yale University24, University of Utah25, University of Washington26, University of Pittsburgh27, University of California, Irvine28, Veterans Health Administration29, University of Rochester30, University of Toulouse31, German Cancer Research Center32, Goethe University Frankfurt33, National and Kapodistrian University of Athens34, University of Bologna35, Utrecht University36, Guy's Hospital37, King's College London38, University of Cambridge39, University of Manchester40, Newcastle University41, University of Oxford42, University of Illinois at Chicago43, University of Michigan44, Centre Hospitalier Universitaire de Toulouse45, McGill University46, Autism Speaks47
TL;DR: Linkage and copy number variation analyses implicate chromosome 11p12–p13 and neurexins, respectively, among other candidate loci, highlighting glutamate-related genes as promising candidates for contributing to ASDs.
Abstract: Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.
1,338 citations
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TL;DR: This work develops an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast, and observes that at given levels of sensitivity, the predictions are more accurate than the existing high-throughput experimental data sets.
Abstract: We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.
1,338 citations
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TL;DR: This study examined the relative importance of each component to ratings of overall performance by using an experimental policy-capturing design to read hypothetical profiles describing employees' task, citizenship, and counterproductive performance and provided global ratings of performance.
Abstract: A review of research on job performance suggests 3 broad components: task, citizenship, and counterproductive performance. This study examined the relative importance of each component to ratings of overall performance by using an experimental policy-capturing design. Managers in 5 jobs read hypothetical profiles describing employees' task, citizenship, and counterproductive performance and provided global ratings of performance. Within-subjects regression analyses indicated that the weights given to the 3 performance components varied across raters. Hierarchical cluster analyses indicated that raters' policies could be grouped into 3 homogeneous clusters: (a) task performance weighted highest, (b) counterproductive performance weighted highest, and (c) equal and large weights given to task and counterproductive performance. Hierarchical linear modeling indicated that demographic variables were not related to raters' weights.
1,338 citations
Authors
Showing all 127245 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gordon H. Guyatt | 231 | 1620 | 228631 |
David J. Hunter | 213 | 1836 | 207050 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Thomas C. Südhof | 191 | 653 | 118007 |
Gordon B. Mills | 187 | 1273 | 186451 |
George Efstathiou | 187 | 637 | 156228 |
John P. A. Ioannidis | 185 | 1311 | 193612 |
Paul M. Thompson | 183 | 2271 | 146736 |
Yusuke Nakamura | 179 | 2076 | 160313 |
Chris Sander | 178 | 713 | 233287 |
David R. Williams | 178 | 2034 | 138789 |
David L. Kaplan | 177 | 1944 | 146082 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Deborah J. Cook | 173 | 907 | 148928 |