Institution
Oklahoma State University–Stillwater
Education•Stillwater, Oklahoma, United States•
About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.
Papers published on a yearly basis
Papers
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TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.
12,798 citations
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TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.
9,282 citations
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29 Dec 1995TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Abstract: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
6,463 citations
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Broad Institute1, Commonwealth Scientific and Industrial Research Organisation2, Massachusetts Institute of Technology3, Hebrew University of Jerusalem4, Science for Life Laboratory5, Pittsburgh Supercomputing Center6, Oklahoma State University–Stillwater7, Griffith University8, University of Wisconsin-Madison9, Dresden University of Technology10, California Institute for Quantitative Biosciences11, Flanders Institute for Biotechnology12, Parco Tecnologico Padano13, United States Department of Agriculture14, Purdue University15, Indiana University16
TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Abstract: De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
6,369 citations
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TL;DR: In the Global Burden of Disease Study 2013 (GBD 2013) as discussed by the authors, the authors used the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data.
5,792 citations
Authors
Showing all 18403 results
Name | H-index | Papers | Citations |
---|---|---|---|
L D Yates | 1 | 1 | 265 |
Dustin G. Loftis | 1 | 1 | 12 |
R.M. McKenzie | 1 | 1 | 3 |
A.R. Schulz | 1 | 1 | 13 |
and Shankar Subramaniam | 1 | 1 | 54 |
Emily N. Ackland | 1 | 1 | 9 |
Gladstone K. Dzata | 1 | 1 | 20 |
Katharine M. Kocan | 1 | 1 | 44 |
T.M. Wilson | 1 | 1 | 3 |
Douglas Morris | 1 | 1 | 19 |
H. Saidi | 1 | 1 | 5 |
E. Katsalirou | 1 | 1 | 44 |
J.A. Rummage | 1 | 1 | 29 |
P. J. Mahasreshti | 1 | 1 | 33 |
C. T. Haan | 1 | 1 | 6 |