Institution
Griffith University
Education•Brisbane, Queensland, Australia•
About: Griffith University is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 13830 authors who have published 49318 publications receiving 1420865 citations.
Topics: Population, Context (language use), Poison control, Health care, Tourism
Papers published on a yearly basis
Papers
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TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
7,090 citations
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TL;DR: Prevalence and severity of health loss were weakly correlated and age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010, but population growth and ageing have increased YLD numbers and crude rates over the past two decades.
7,021 citations
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TL;DR: The results for 1990 and 2010 supersede all previously published Global Burden of Disease results and highlight the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account.
6,861 citations
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Broad Institute1, Commonwealth Scientific and Industrial Research Organisation2, Hebrew University of Jerusalem3, Massachusetts Institute of Technology4, 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: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
5,802 citations
Authors
Showing all 14162 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rasmus Nielsen | 135 | 556 | 84898 |
Claudiu T. Supuran | 134 | 1973 | 86850 |
Jeffrey D. Sachs | 130 | 692 | 86589 |
David Smith | 129 | 2184 | 100917 |
Michael R. Green | 126 | 537 | 57447 |
John J. McGrath | 120 | 791 | 124804 |
E. K. U. Gross | 119 | 1154 | 75970 |
David M. Evans | 116 | 632 | 74420 |
Mike Clarke | 113 | 1037 | 164328 |
Wayne Hall | 111 | 1260 | 75606 |
Patrick J. McGrath | 107 | 681 | 51940 |
Peter K. Smith | 107 | 855 | 49174 |
Erko Stackebrandt | 106 | 633 | 68201 |
Phyllis Butow | 102 | 731 | 37752 |
John Quackenbush | 99 | 427 | 67029 |