Institution
Rutgers University
Education•New Brunswick, New Jersey, United States•
About: Rutgers University is a education organization based out in New Brunswick, New Jersey, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 68736 authors who have published 159418 publications receiving 6713860 citations. The organization is also known as: Rutgers, The State University of New Jersey & Rutgers.
Topics: Population, Poison control, Health care, Cancer, Galaxy
Papers published on a yearly basis
Papers
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Brown University1, Stanford University2, University of New Mexico3, University of Connecticut4, University of Southern California5, University of California, Merced6, University of Washington7, National Science Foundation8, Los Alamos National Laboratory9, Rutgers University10, Columbia University11, University of Bergen12, Portland State University13, University of Kansas14
TL;DR: Current evidence confirms that, as proposed by the Baas-Becking hypothesis, 'the environment selects' and is, in part, responsible for spatial variation in microbial diversity, but recent studies also dispute the idea that 'everything is everywhere'.
Abstract: We review the biogeography of microorganisms in light of the biogeography of macroorganisms A large body of research supports the idea that free-living microbial taxa exhibit biogeographic patterns Current evidence confirms that, as proposed by the Baas-Becking hypothesis, 'the environment selects' and is, in part, responsible for spatial variation in microbial diversity However, recent studies also dispute the idea that 'everything is everywhere' We also consider how the processes that generate and maintain biogeographic patterns in macroorganisms could operate in the microbial world
2,456 citations
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Stanford University1, Harvard University2, University of Florida3, University of Washington4, University of Texas Medical Branch5, University of Colorado Denver6, University of Texas Southwestern Medical Center7, University of Rochester8, University of Pittsburgh9, University of Toronto10, University of California, San Francisco11, Loyola University Chicago12, Washington University in St. Louis13, Rutgers University14
TL;DR: This study shows that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another.
Abstract: A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
2,438 citations
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01 Jan 2021TL;DR: Transfer learning aims to improve the performance of target learners on target domains by transferring the knowledge contained in different but related source domains as discussed by the authors, in which the dependence on a large number of target-domain data can be reduced for constructing target learners.
Abstract: Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.
2,433 citations
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01 Jan 2007TL;DR: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.
Abstract: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
2,428 citations
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TL;DR: Systems T he Internet offers vast new opportunities to interact with total strangers, but these interactions can be fun, informative, even profitable, but they also involve risk.
Abstract: Systems T he Internet offers vast new opportunities to interact with total strangers. These interactions can be fun, informative, even profitable. But they also involve risk. Is the advice of a self-proclaimed expert at expertcentral.com reliable? Will an unknown dotcom site or eBay seller ship items promptly with appropriate packaging? Will the product be the same one described online? Prior to the Internet, such questions were answered, in part, through personal and corporate reputations. Vendors provided references, Better Business Bureaus tallied complaints, and past personal experience and person-to-person gossip told you on whom you could rely and on whom you could not. Participants’ standing in their communities, including their roles in church and civic organizations, served as a valuable hostage. Internet services operate on a vastly larger scale
2,410 citations
Authors
Showing all 69437 results
Name | H-index | Papers | Citations |
---|---|---|---|
Salim Yusuf | 231 | 1439 | 252912 |
Daniel Levy | 212 | 933 | 194778 |
Eugene V. Koonin | 199 | 1063 | 175111 |
Eric Boerwinkle | 183 | 1321 | 170971 |
David L. Kaplan | 177 | 1944 | 146082 |
Derek R. Lovley | 168 | 582 | 95315 |
Mark Gerstein | 168 | 751 | 149578 |
Gang Chen | 167 | 3372 | 149819 |
Hongfang Liu | 166 | 2356 | 156290 |
Robert Stone | 160 | 1756 | 167901 |
Mark E. Cooper | 158 | 1463 | 124887 |
Michael B. Sporn | 157 | 559 | 94605 |
Cumrun Vafa | 157 | 509 | 88515 |
Wolfgang Wagner | 156 | 2342 | 123391 |
David M. Sabatini | 155 | 413 | 135833 |