Institution
Stanford University
Education•Stanford, California, United States•
About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.
Topics: Population, Transplantation, Medicine, Cancer, Gene
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors present an integrative theoretical framework to explain and predict psychological changes achieved by different modes of treatment, including enactive, vicarious, exhortative, and emotive sources.
16,833 citations
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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
16,538 citations
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TL;DR: In this paper, the authors consider the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning and examine some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space.
Abstract: This paper considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space, and the effects of ecological interaction. Two general situations involving the development and use of knowledge in organizations are modeled. The first is the case of mutual learning between members of an organization and an organizational code. The second is the case of learning and competitive advantage in competition for primacy. The paper develops an argument that adaptive processes, by refining exploitation more rapidly than exploration, are likely to become effective in the short run but self-destructive in the long run. The possibility that certain common organizational practices ameliorate that tendency is assessed.
16,377 citations
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TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Abstract: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is de- scribed that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be inter- preted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly charac- terized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
16,371 citations
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TL;DR: In this paper, Shulman observa la historia de evaluaciones docentes, noting that the evaluación docente parecia preocuparse tanto por los conocimientos, como el siglo anterior se preoccupaba por la pedagogia.
Abstract: Este articulo fue un discurso presidencial en la reunion de America Educational Research Association de Chicago el ano 1985. -- Curioso sobre el por que el publico a menudo tiene una baja opinion sobre el conocimiento de los profesores, Shulman observa la historia de evaluaciones docentes. En la segunda mitad del 1800, las evaluaciones para quienes deseaban ensenar se basaban casi por completo en contenido. Para el ano en que el autor escribe el articulo, en 1985, la evaluacion era completamente distinta. En lugar de enfocarse en contenido, se enfocaba en topicos como planificacion de clases, sensibilizacion cultural, y otros aspectos de la conducta docente. Mientras los topicos usualmente tenian raices en la investigacion, claramente no representan el amplio espectro de habilidades y conocimientos que un docente necesita para ser efectivo. Mas especificamente, para los anos 80', la evaluacion docente parecia preocuparse tanto por los conocimientos, como el siglo anterior se preocupaba por la pedagogia.
15,740 citations
Authors
Showing all 127468 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
George M. Whitesides | 240 | 1739 | 269833 |
Yi Cui | 220 | 1015 | 199725 |
Yi Chen | 217 | 4342 | 293080 |
David Miller | 203 | 2573 | 204840 |
David Baltimore | 203 | 876 | 162955 |
Edward Witten | 202 | 602 | 204199 |
Irving L. Weissman | 201 | 1141 | 172504 |
Hongjie Dai | 197 | 570 | 182579 |
Robert M. Califf | 196 | 1561 | 167961 |
Frank E. Speizer | 193 | 636 | 135891 |
Thomas C. Südhof | 191 | 653 | 118007 |
Gad Getz | 189 | 520 | 247560 |
Mark Hallett | 186 | 1170 | 123741 |
John P. A. Ioannidis | 185 | 1311 | 193612 |