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Institution

Fujitsu

CompanyMinato, Japan
About: Fujitsu is a company organization based out in Minato, Japan. It is known for research contribution in the topics: Signal & Transmission (telecommunications). The organization has 47462 authors who have published 75050 publications receiving 827558 citations. The organization is also known as: Fujitsu Ltd. & Fujitsu Limited.


Papers
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Journal ArticleDOI
Minoru Kanehisa1, Miho Furumichi1, Mao Tanabe1, Yoko Sato2, Kanae Morishima1 
TL;DR: The content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases, and the newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined.
Abstract: KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

5,741 citations

Journal ArticleDOI
TL;DR: The KEGG GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes, and new automatic annotation servers, BlastKOalA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from theGENES database.
Abstract: KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.

4,847 citations

Journal ArticleDOI
TL;DR: Both BlastKOALA and GhostKOalA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and K EGG modules to infer high-level functions of the organism or the ecosystem.

2,247 citations

Journal ArticleDOI
TL;DR: The K EGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database, and the KO database for functional orthologs continues to be improved.
Abstract: KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

2,087 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an overview of plane-wave pseudopotential density functional theory (DFT) methods applicable to studies of large periodic systems and present a number of algorithmic implementations, including ultrasoft pseudopotentials, efficient iterative schemes for solving the one-electron DFT equations, and computationally efficient codes for massively parallel computers.
Abstract: Recent developments in density functional theory (DFT) methods applicable to studies of large periodic systems are outlined. During the past three decades, DFT has become an essential part of computational materials science, addressing problems in materials design and processing. The theory allows us to interpret experimental data and to generate property data (such as binding energies of molecules on surfaces) for known materials, and also serves as an aid in the search for and design of novel materials and processes. A number of algorithmic implementations are currently being used, including ultrasoft pseudopotentials, efficient iterative schemes for solving the one-electron DFT equations, and computationally efficient codes for massively parallel computers. The first part of this article provides an overview of plane-wave pseudopotential DFT methods. Their capabilities are subsequently illustrated by examples including the prediction of crystal structures, the study of the compressibility of minerals, and applications to pressure-induced phase transitions. Future theoretical and computational developments are expected to lead to improved accuracy and to treatment of larger systems with a higher computational efficiency. c 2000 John Wiley & Sons, Inc. Int J Quant Chem 77: 895-910, 2000

1,514 citations


Authors

Showing all 47501 results

NameH-indexPapersCitations
John C. Mitchell10467636467
Hideaki Katagiri10131840418
Alberto Sangiovanni-Vincentelli9993445201
Lei Liu98204151163
Takashi Yamamoto84140135169
Naoyuki Takahashi7922432741
Katsushi Ikeuchi7863620622
Shinji Tanaka7763822745
Gerhard Abstreiter7779125631
Robert K. Brayton7142622165
Wenwu Zhu7152320164
Alexei Gruverman6930118610
Manfred K. Warmuth6623918904
Ove Christiansen6521915329
Zhi-Li Zhang6339513181
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202313
202213
2021358
2020712
20191,323
20181,873