Institution
University of British Columbia
Education•Vancouver, British Columbia, Canada•
About: University of British Columbia is a education organization based out in Vancouver, British Columbia, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 89939 authors who have published 209679 publications receiving 9226862 citations. The organization is also known as: UBC & The University of British Columbia.
Papers published on a yearly basis
Papers
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Wellcome Trust Sanger Institute1, Wellcome Trust2, Cambridge University Hospitals NHS Foundation 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, French Institute of Health and Medical Research14, Paris Descartes University15, 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, University of New South Wales29, Bankstown Lidcombe Hospital30, 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
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TL;DR: Prospect theory as mentioned in this paper is an alternative to the classical utility theory of choice, and has been used to explain many complex, real-world puzzles, such as the principles of legal compensation, the equity premium puzzle in financial markets, and the number of hours that New York cab drivers choose to drive on rainy days.
Abstract: This book presents the definitive exposition of 'prospect theory', a compelling alternative to the classical utility theory of choice. Building on the 1982 volume, Judgement Under Uncertainty, this book brings together seminal papers on prospect theory from economists, decision theorists, and psychologists, including the work of the late Amos Tversky, whose contributions are collected here for the first time. While remaining within a rational choice framework, prospect theory delivers more accurate, empirically verified predictions in key test cases, as well as helping to explain many complex, real-world puzzles. In this volume, it is brought to bear on phenomena as diverse as the principles of legal compensation, the equity premium puzzle in financial markets, and the number of hours that New York cab drivers choose to drive on rainy days. Theoretically elegant and empirically robust, this volume shows how prospect theory has matured into a new science of decision making.
7,802 citations
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University of California, Berkeley1, Stellenbosch University2, University of Jyväskylä3, University of Cambridge4, Google5, University of Toronto6, University of Birmingham7, Temple University8, University of British Columbia9, Amazon.com10, University of Georgia11, University of Oxford12, Los Alamos National Laboratory13, University of California, Irvine14
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
Abstract: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
7,624 citations
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TL;DR: Today there is a need for more exact criteria than existed earlier in order to conduct therapeutic trials in multicenter programs, to compare epidemiological surveys, to evaluate new diagnostic procedures, and to estimate the activity of the disease process in MS.
Abstract: Several schemes for the diagnosis and clinical classification of multiple sclerosis (MS) have been advanced [l}. The best known is that published by Schumacher et alC31. The criteria for this scheme were established in order to select patients for participation in therapeutic trials, and pertain only to what might be called definite MS. No provision was made for incorporating supportive laboratory data into the diagnostic criteria. As no reliable specific laboratory test for the diagnosis of MS has been discovered, the diagnosis remains a clinical one, and there is still a need for clinical diagnostic criteria. However, several laboratory and clinical procedures have been developed within the last decade which aid greatly in demonstrating neurological dysfunction attributable to lesions, and even the lesions themselves. One problem with the various published diagnostic classifications is their discrepant terminology: what is considered “probable” in one is called “definite” in another. Another problem is that all the proposed schemes require much subjective judgment, a difficulty which cannot be completely overcome but can be diminished by adding to the clinical evaluation the results of laboratory, neuroimaging, neuropsychological, and neurophysiological procedures. Today there is a need for more exact criteria than existed earlier in order to conduct therapeutic trials in multicenter programs, to compare epidemiological surveys, to evaluate new diagnostic procedures, and to estimate the activity of the disease process in MS. Method and Procedure
7,565 citations
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TL;DR: The review as discussed by the authors summarizes much of particle physics and cosmology using data from previous editions, plus 3,283 new measurements from 899 Japers, including the recently discovered Higgs boson, leptons, quarks, mesons and baryons.
Abstract: The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,283 new measurements from 899 Japers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as heavy neutrinos, supersymmetric and technicolor particles, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Supersymmetry, Extra Dimensions, Particle Detectors, Probability, and Statistics. Among the 112 reviews are many that are new or heavily revised including those on: Dark Energy, Higgs Boson Physics, Electroweak Model, Neutrino Cross Section Measurements, Monte Carlo Neutrino Generators, Top Quark, Dark Matter, Dynamical Electroweak Symmetry Breaking, Accelerator Physics of Colliders, High-Energy Collider Parameters, Big Bang Nucleosynthesis, Astrophysical Constants and Cosmological Parameters.
7,337 citations
Authors
Showing all 90682 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gordon H. Guyatt | 231 | 1620 | 228631 |
John C. Morris | 183 | 1441 | 168413 |
Douglas Scott | 178 | 1111 | 185229 |
John R. Yates | 177 | 1036 | 129029 |
Deborah J. Cook | 173 | 907 | 148928 |
Richard A. Gibbs | 172 | 889 | 249708 |
Evan E. Eichler | 170 | 567 | 150409 |
James F. Sallis | 169 | 825 | 144836 |
Michael Snyder | 169 | 840 | 130225 |
Jiawei Han | 168 | 1233 | 143427 |
Michael Kramer | 167 | 1713 | 127224 |
Bruce L. Miller | 163 | 1153 | 115975 |
Peter A. R. Ade | 162 | 1387 | 138051 |
Marc W. Kirschner | 162 | 457 | 102145 |
Kaj Blennow | 160 | 1845 | 116237 |