Institution
New York University
Education•New York, New York, United States•
About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.
Topics: Population, Poison control, Health care, Cancer, Mental health
Papers published on a yearly basis
Papers
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Broad Institute1, Harvard University2, Monash University3, Kyoto University4, Genentech5, Vanderbilt University6, New York University7, NewYork–Presbyterian Hospital8, Second Military Medical University9, University of Queensland10, University of Toronto11, University of Groningen12, University of Tartu13, Beijing Jiaotong University14, Icahn School of Medicine at Mount Sinai15, Radboud University Nijmegen16, Medisch Spectrum Twente17, Leiden University18, University of Paris19, French Institute of Health and Medical Research20, University of Alabama at Birmingham21, University of Cambridge22, University of Amsterdam23, GlaxoSmithKline24, Hanyang University25, Spanish National Research Council26, Complutense University of Madrid27, Umeå University28, Boston University29, Council on Education for Public Health30, McGill University31, University of Manchester32, National Health Service33, University of Pittsburgh34, University of California, San Francisco35, Karolinska Institutet36, North Shore-LIJ Health System37, University of Chicago38, University of Tokyo39
TL;DR: A genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries provides empirical evidence that the genetics of RA can provide important information for drug discovery, and sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis.
Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
1,910 citations
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TL;DR: Four-dimensional covariant nonlinear theories of massive gravity are constructed which are ghost-free in the decoupling limit to all orders, and the Hamiltonian constraint is maintained at least up to and including quartic order in nonlinearities, hence excluding the possibility of the Boulware-Deser ghost up to this order.
Abstract: We construct four-dimensional covariant nonlinear theories of massive gravity which are ghost-free in the decoupling limit to all orders. These theories resum explicitly all the nonlinear terms of an effective field theory of massive gravity. We show that away from the decoupling limit the Hamiltonian constraint is maintained at least up to and including quartic order in nonlinearities, hence excluding the possibility of the Boulware-Deser ghost up to this order. We also show that the same remains true to all orders in a similar toy model.
1,909 citations
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TL;DR: In this article, a method called cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is proposed, in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout.
Abstract: High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.
1,904 citations
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TL;DR: In this article, the authors developed a model of board processes by integrating the literature on boards of directors with group dynamics and workgroup effectiveness, and the resulting model illuminates the complexity of board dynamics and paves the way for future empirical research that expands and refines our understanding of what makes boards effective.
Abstract: Recent research developments underscore the need for research on the processes that link board demography with firm performance. In this article we develop a model of board processes by integrating the literature on boards of directors with the literature on group dynamics and workgroup effectiveness. The resulting model illuminates the complexity of board dynamics and paves the way for future empirical research that expands and refines our understanding of what makes boards effective.
1,900 citations
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07 Dec 2015TL;DR: A generative parametric model capable of producing high quality samples of natural images using a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion.
Abstract: In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset.
1,898 citations
Authors
Showing all 73237 results
Name | H-index | Papers | Citations |
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Rob Knight | 201 | 1061 | 253207 |
Virginia M.-Y. Lee | 194 | 993 | 148820 |
Frank E. Speizer | 193 | 636 | 135891 |
Stephen V. Faraone | 188 | 1427 | 140298 |
Eric R. Kandel | 184 | 603 | 113560 |
Andrei Shleifer | 171 | 514 | 271880 |
Eliezer Masliah | 170 | 982 | 127818 |
Roderick T. Bronson | 169 | 679 | 107702 |
Timothy A. Springer | 167 | 669 | 122421 |
Alvaro Pascual-Leone | 165 | 969 | 98251 |
Nora D. Volkow | 165 | 958 | 107463 |
Dennis R. Burton | 164 | 683 | 90959 |
Charles N. Serhan | 158 | 728 | 84810 |
Giacomo Bruno | 158 | 1687 | 124368 |
Tomas Hökfelt | 158 | 1033 | 95979 |