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Institution

Massachusetts Institute of Technology

EducationCambridge, Massachusetts, United States
About: Massachusetts Institute of Technology is a(n) education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topic(s): Population & Laser. The organization has 116795 authors who have published 268000 publication(s) receiving 18272025 citation(s). The organization is also known as: MIT & M.I.T..
Topics: Population, Laser, Galaxy, Gene, Scattering
Papers
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Journal ArticleDOI
04 Mar 2011-Cell
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
Abstract: The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.

42,275 citations


Book
01 Jan 1988-
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

32,257 citations


Journal ArticleDOI
01 Jul 2012-Nature Methods
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

30,888 citations


Journal ArticleDOI
23 Jan 2004-Cell
TL;DR: Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.
Abstract: MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.

30,422 citations


Journal ArticleDOI
07 Jan 2000-Cell
TL;DR: This work has been supported by the Department of the Army and the National Institutes of Health, and the author acknowledges the support and encouragement of the National Cancer Institute.
Abstract: We wish to thank Terry Schoop of Biomed Arts Associates, San Francisco, for preparation of the figures, Cori Bargmann and Zena Werb for insightful comments on the manuscript, and Normita Santore for editorial assistance. In addition, we are indebted to Joe Harford and Richard Klausner, who allowed us to adapt and expand their depiction of the cell signaling network, and we appreciate suggestions on signaling pathways from Randy Watnick, Brian Elenbas, Bill Lundberg, Dave Morgan, and Henry Bourne. R. A. W. is a Ludwig Foundation and American Cancer Society Professor of Biology. His work has been supported by the Department of the Army and the National Institutes of Health. D. H. acknowledges the support and encouragement of the National Cancer Institute. Editorial policy has rendered the citations illustrative but not comprehensive.

26,950 citations


Authors

Showing all 116795 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Robert Langer2812324326306
George M. Whitesides2401739269833
Trevor W. Robbins2311137164437
George Davey Smith2242540248373
Yi Cui2201015199725
Robert J. Lefkowitz214860147995
David J. Hunter2131836207050
Daniel Levy212933194778
Rudolf Jaenisch206606178436
Mark J. Daly204763304452
David Miller2032573204840
David Baltimore203876162955
Rakesh K. Jain2001467177727
Ronald M. Evans199708166722
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2022141
202110,566
202011,920
201911,205
201810,883
201710,505

Top Attributes

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Institution's top 5 most impactful journals

Social Science Research Network

5.3K papers, 337.8K citations

Physical Review Letters

3.8K papers, 425.2K citations

The Astrophysical Journal

2.6K papers, 226.6K citations

Nature

2.4K papers, 814.9K citations