Institution
University of Cambridge
Education•Cambridge, United Kingdom•
About: University of Cambridge is a education organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 118293 authors who have published 282289 publications receiving 14497093 citations. The organization is also known as: Cambridge University & Cambridge.
Topics: Population, Galaxy, Context (language use), Gene, Transplantation
Papers published on a yearly basis
Papers
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01 Jan 1965TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Abstract: Algebra of Vectors and Matrices. Probability Theory, Tools and Techniques. Continuous Probability Models. The Theory of Least Squares and Analysis of Variance. Criteria and Methods of Estimation. Large Sample Theory and Methods. Theory of Statistical Inference. Multivariate Analysis. Publications of the Author. Author Index. Subject Index.
8,300 citations
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TL;DR: The establishment in tissue culture of pluripotent cell lines which have been isolated directly from in vitro cultures of mouse blastocysts are reported, able to differentiate either in vitro or after innoculation into a mouse as a tumour in vivo.
Abstract: Pluripotential cells are present in a mouse embryo until at least an early post-implantation stage, as shown by their ability to take part hi the formation of chimaeric animals1 and to form teratocarcinomas2. Until now it has not been possible to establish progressively growing cultures of these cells in vitro, and cell lines have only been obtained after teratocarcinoma formation in vivo. We report here the establishment in tissue culture of pluripotent cell lines which have been isolated directly from in vitro cultures of mouse blastocysts. These cells are able to differentiate either in vitro or after innoculation into a mouse as a tumour in vivo. They have a normal karyotype.
8,144 citations
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TL;DR: The Protein Data Bank is a computer-based archival file for macromolecular structures that stores in a uniform format atomic co-ordinates and partial bond connectivities, as derived from crystallographic studies.
7,983 citations
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15 Feb 2018
TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Abstract: We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).
7,904 citations
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Wellcome Trust Sanger Institute1, Cambridge University Hospitals NHS Foundation Trust2, Wellcome Trust3, University of British Columbia4, University of Cambridge5, Oslo University Hospital6, The Breast Cancer Research Foundation7, University of Oslo8, University of Münster9, Université libre de Bruxelles10, German Cancer Research Center11, University of Iceland12, Erasmus University Rotterdam13, Paris Descartes University14, French Institute of Health and Medical Research15, University of Paris16, Broad Institute17, University of Bergen18, University of Oviedo19, University of Queensland20, University of Glasgow21, Harvard University22, United States Department of Veterans Affairs23, Netherlands Cancer Institute24, University of Kiel25, Radboud University Nijmegen26, King's College London27, Curie Institute28, Bankstown Lidcombe Hospital29, University of New South Wales30, University of Barcelona31
TL;DR: It is shown that hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types, and this results reveal the diversity of mutational processes underlying the development of cancer.
Abstract: All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.
7,904 citations
Authors
Showing all 119522 results
Name | H-index | Papers | Citations |
---|---|---|---|
Albert Hofman | 267 | 2530 | 321405 |
Zhong Lin Wang | 245 | 2529 | 259003 |
Solomon H. Snyder | 232 | 1222 | 200444 |
Trevor W. Robbins | 231 | 1137 | 164437 |
George Davey Smith | 224 | 2540 | 248373 |
Nicholas J. Wareham | 212 | 1657 | 204896 |
Cyrus Cooper | 204 | 1869 | 206782 |
Eric B. Rimm | 196 | 988 | 147119 |
Martin White | 196 | 2038 | 232387 |
Simon D. M. White | 189 | 795 | 231645 |
Michael Rutter | 188 | 676 | 151592 |
George Efstathiou | 187 | 637 | 156228 |
Mark Hallett | 186 | 1170 | 123741 |
David H. Weinberg | 183 | 700 | 171424 |
Paul G. Richardson | 183 | 1533 | 155912 |