Institution
University of Copenhagen
Education•Copenhagen, Denmark•
About: University of Copenhagen is a education organization based out in Copenhagen, Denmark. It is known for research contribution in the topics: Population & Galaxy. The organization has 57645 authors who have published 149740 publications receiving 5903093 citations. The organization is also known as: Copenhagen University & Københavns Universitet.
Topics: Population, Galaxy, Insulin, Skeletal muscle, Diabetes mellitus
Papers published on a yearly basis
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TL;DR: eggNOG as discussed by the authors is a public database of orthology relationships, gene evolutionary histories and functional annotations, with a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes.
Abstract: eggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de.
1,971 citations
TL;DR: Glucosinolates are sulfur-rich, anionic natural products that upon hydrolysis by endogenous thioglucosidases called myrosinases produce several different products that function as cancer-preventing agents, biopesticides, and flavor compounds.
Abstract: Glucosinolates are sulfur-rich, anionic natural products that upon hydrolysis by endogenous thioglucosidases called myrosinases produce several different products (e.g., isothiocyanates, thiocyanates, and nitriles). The hydrolysis products have many different biological activities, e.g., as defense compounds and attractants. For humans these compounds function as cancer-preventing agents, biopesticides, and flavor compounds. Since the completion of the Arabidopsis genome, glucosinolate research has made significant progress, resulting in near-complete elucidation of the core biosynthetic pathway, identification of the first regulators of the pathway, metabolic engineering of specific glucosinolate profiles to study function, as well as identification of evolutionary links to related pathways. Although much has been learned in recent years, much more awaits discovery before we fully understand how and why plants synthesize glucosinolates. This may enable us to more fully exploit the potential of these compounds in agriculture and medicine.
1,955 citations
TL;DR: This review describes and compares the theoretical and algorithmic foundations of current pre- processing methods plus the qualitative and quantitative consequences of their application to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.
Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.
1,942 citations
University of Chicago1, Pierre-and-Marie-Curie University2, Lawrence Berkeley National Laboratory3, University of Pennsylvania4, Argonne National Laboratory5, Fermilab6, University of Cape Town7, African Institute for Mathematical Sciences8, Texas A&M University9, University of Portsmouth10, University of Cambridge11, University of Toronto12, Wayne State University13, University of Colorado Boulder14, University of Tokyo15, California Institute of Technology16, University of Victoria17, University of California, Berkeley18, University of Illinois at Urbana–Champaign19, Autonomous University of Barcelona20, University of Chile21, Stockholm University22, University of Texas at Austin23, Princeton University24, University of Oxford25, Las Cumbres Observatory Global Telescope Network26, University of California, Santa Barbara27, Rutgers University28, University of Copenhagen29, Australian Astronomical Observatory30, Instituto Superior Técnico31, University of Utah32, Rochester Institute of Technology33, Space Telescope Science Institute34, Johns Hopkins University35, Pennsylvania State University36, University of the Western Cape37, University of Southampton38
TL;DR: In this article, the authors presented cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations.
Abstract: Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05
1,939 citations
TL;DR: The International Standards for Neurological Classification of Spinal Cord Injury (ISC-II) as mentioned in this paper is a set of international standards for the classification of spinal cord injury that were developed by the International Association of Neurological Diseases and Pathology (IANS).
Abstract: (2003). International Standards For Neurological Classification Of Spinal Cord Injury. The Journal of Spinal Cord Medicine: Vol. 26, No. sup1, pp. S50-S56.
1,931 citations
Authors
Showing all 58387 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Michael Karin | 236 | 704 | 226485 |
| Matthias Mann | 221 | 887 | 230213 |
| Peer Bork | 206 | 697 | 245427 |
| Ronald Klein | 194 | 1305 | 149140 |
| Kenneth S. Kendler | 177 | 1327 | 142251 |
| Dorret I. Boomsma | 176 | 1507 | 136353 |
| Ramachandran S. Vasan | 172 | 1100 | 138108 |
| Unnur Thorsteinsdottir | 167 | 444 | 121009 |
| Mika Kivimäki | 166 | 1515 | 141468 |
| Jun Wang | 166 | 1093 | 141621 |
| Anders Björklund | 165 | 769 | 84268 |
| Gerald I. Shulman | 164 | 579 | 109520 |
| Jaakko Kaprio | 163 | 1532 | 126320 |
| Veikko Salomaa | 162 | 843 | 135046 |
| Daniel J. Jacob | 162 | 656 | 76530 |