Institution
University of Amsterdam
Education•Amsterdam, Noord-Holland, Netherlands•
About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.
Papers published on a yearly basis
Papers
More filters
••
University of Groningen1, Columbia University2, University of Washington3, Leiden University4, University of Amsterdam5, Erasmus University Rotterdam6, Max Planck Society7, Utrecht University8, Centrum Wiskunde & Informatica9, Radboud University Nijmegen10, Massachusetts Institute of Technology11, Harvard University12, Pfizer13, Beijing Institute of Genomics14, University of Copenhagen15
TL;DR: The Genome of the Netherlands (GoNL) Project is described, in which the whole genomes of 250 Dutch parent-offspring families were sequenced and a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions were constructed.
Abstract: Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage (∼13×) and trio design enabled extensive characterization of structural variation, including midsize events (30-500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.
677 citations
••
University of Duisburg-Essen1, University of Düsseldorf2, Harvard University3, University of Warsaw4, St. Vincent's Institute of Medical Research5, University of Melbourne6, Johns Hopkins University7, Swiss Institute of Bioinformatics8, Western General Hospital9, The Turing Institute10, BC Cancer Agency11, University of British Columbia12, ETH Zurich13, Delft University of Technology14, Leiden University Medical Center15, Broad Institute16, Georgia State University17, Heidelberg Institute for Theoretical Studies18, Karlsruhe Institute of Technology19, Centrum Wiskunde & Informatica20, Utrecht University21, University of Amsterdam22, Imperial College London23, Radboud University Nijmegen24, University Medical Center Groningen25, Wageningen University and Research Centre26, University of Connecticut27, European Bioinformatics Institute28, Wellcome Trust Sanger Institute29, University of Cambridge30, Max Planck Society31, Saarland University32, Zuse Institute Berlin33, German Cancer Research Center34, Leiden University35, I.M. Sechenov First Moscow State Medical University36, Princeton University37, Memorial Sloan Kettering Cancer Center38
TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.
Abstract: The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
677 citations
••
11 Sep 2017TL;DR: A version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs, is proposed, observing that GCN layers are complementary to LSTM ones.
Abstract: Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.
677 citations
••
Karolinska Institutet1, Ghent University2, Eastern Virginia Medical School3, Université de Montréal4, Katholieke Universiteit Leuven5, Regeneron6, University of Pittsburgh7, University of Barcelona8, University of Pennsylvania9, Brigham and Women's Hospital10, Royal Brisbane and Women's Hospital11, St Thomas' Hospital12, Charité13, Humanitas University14, University of Pisa15, University of South Florida16, University of Amsterdam17, University of Fukui18
TL;DR: Dupilumab significantly improved the coprimary endpoints in both studies and was added to standard of care in adults with severe CRSwNP despite previous treatment with systemic corticosteroids, surgery, or both.
676 citations
••
TL;DR: In this article, the authors discuss the impact of such constraints on possible applications of scalar singlet dark matter, including a strong electroweak phase transition, and the question of vacuum stability of the Higgs potential at high scales.
Abstract: One of the simplest models of dark matter is where a scalar singlet field S comprises some or all of the dark matter and interacts with the standard model through an vertical bar H vertical bar S-2(2) coupling to the Higgs boson. We update the present limits on the model from LHC searches for invisible Higgs decays, the thermal relic density of S, and dark matter searches via indirect and direct detection. We point out that the currently allowed parameter space is on the verge of being significantly reduced with the next generation of experiments. We discuss the impact of such constraints on possible applications of scalar singlet dark matter, including a strong electroweak phase transition, and the question of vacuum stability of the Higgs potential at high scales.
676 citations
Authors
Showing all 59759 results
Name | H-index | Papers | Citations |
---|---|---|---|
Richard A. Flavell | 231 | 1328 | 205119 |
Scott M. Grundy | 187 | 841 | 231821 |
Stuart H. Orkin | 186 | 715 | 112182 |
Kenneth C. Anderson | 178 | 1138 | 126072 |
David A. Weitz | 178 | 1038 | 114182 |
Dorret I. Boomsma | 176 | 1507 | 136353 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Michael Kramer | 167 | 1713 | 127224 |
Nicholas J. White | 161 | 1352 | 104539 |
Lex M. Bouter | 158 | 767 | 103034 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Jerome I. Rotter | 156 | 1071 | 116296 |
David Cella | 156 | 1258 | 106402 |
David Eisenberg | 156 | 697 | 112460 |
Naveed Sattar | 155 | 1326 | 116368 |