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Institution

Stanford University

EducationStanford, California, United States
About: Stanford University is a(n) education organization based out in Stanford, California, United States. It is known for research contribution in the topic(s): Population & Transplantation. The organization has 125751 authors who have published 320347 publication(s) receiving 21892059 citation(s). The organization is also known as: Leland Stanford Junior University & University of Stanford.
Topics: Population, Transplantation, Cancer, Gene, Health care
Papers
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Journal ArticleDOI
01 Apr 1998-
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

14,045 citations


Journal ArticleDOI
TL;DR: The 1971 preliminary criteria for the classification of systemic lupus erythematosus (SLE) were revised and updated to incorporate new immunologic knowledge and improve disease classification and showed gains in sensitivity and specificity.
Abstract: The 1971 preliminary criteria for the classification of systemic lupus erythematosus (SLE) were revised and updated to incorporate new immunologic knowledge and improve disease classification. The 1982 revised criteria include fluorescence antinuclear antibody and antibody to native DNA and Sm antigen. Some criteria involving the same organ systems were aggregated into single criteria. Raynaud's phenomenon and alopecia were not included in the 1982 revised criteria because of low sensitivity and specificity. The new criteria were 96% sensitive and 96% specific when tested with SLE and control patient data gathered from 18 participating clinics. When compared with the 1971 criteria, the 1982 revised criteria showed gains in sensitivity and specificity.

13,962 citations


Journal ArticleDOI
Abstract: This article addresses the centrality of the self-efficacy mechanism in human agency. Self-per- cepts of efficacy influence thought patterns, actions, and emotional arousal. In causal tests the higher the level of induced self-efficacy, the higher the perfor- mance accomplishments and the lower the emotional arousal. Different lines of research are reviewed, show- ing that the self-efficacy mechanism may have wide explanatory power. Perceived self-efficacy helps to ac- count for such diverse phenomena as changes in coping behavior produced by different modes of influence, level of physiological stress reactions, self-regulation of refractory behavior, resignation and despondency to failure experiences, self-debilitating effects of proxy control and illusory inefficaciousness, achievement strivings, growth of intrinsic interest, and career pur- suits. The influential role of perceived collective effi- cacy in social change is analyzed, as are the social con- ditions conducive to development of collective inefficacy. Psychological theorizing and research tend to cen- ter on issues concerning either acquisition of knowledge or execution of response patterns. As a result the processes governing the interrelation- ship between knowledge and action have been largely neglected (Newell, 1978). Some of the re- cent efforts to bridge this gap have been directed at the biomechanics problem—how efferent com- mands of action plans guide the production of ap- propriate response patterns (Stelmach, 1976,1978). Others have approached the matter in terms of algorithmic knowledge, which furnishes guides for executing action sequences (Greeno, 1973; Newell, 1973). ,

13,946 citations


Journal ArticleDOI
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Abstract: Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

13,722 citations


11


Journal ArticleDOI
Abstract: We discuss the following problem given a random sample X = (X 1, X 2,…, X n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X, F), on the basis of the observed data x. (Standard jackknife theory gives an approximate mean and variance in the case R(X, F) = \(\theta \left( {\hat F} \right) - \theta \left( F \right)\), θ some parameter of interest.) A general method, called the “bootstrap”, is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.

13,648 citations


Authors

Showing all 125751 results

NameH-indexPapersCitations
Eric S. Lander301826525976
George M. Whitesides2401739269833
Yi Cui2201015199725
Yi Chen2174342293080
David Miller2032573204840
David Baltimore203876162955
Edward Witten202602204199
Irving L. Weissman2011141172504
Hongjie Dai197570182579
Robert M. Califf1961561167961
Frank E. Speizer193636135891
Thomas C. Südhof191653118007
Gad Getz189520247560
Mark Hallett1861170123741
John P. A. Ioannidis1851311193612
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2022177
202117,830
202018,226
201916,189
201814,684
201714,653

Top Attributes

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Institution's top 5 most impactful journals

Social Science Research Network

6.8K papers, 333.2K citations

bioRxiv

3.6K papers, 19.5K citations

Science

2.5K papers, 719.5K citations

Nature

2.2K papers, 787.1K citations