scholarly journals Global classification of NPGCs

2014 ◽  
Vol 13 (12) ◽  
pp. 888-888
Author(s):  
Sarah Crunkhorn
Keyword(s):  
Global Classification

scholarly journals An inshore–offshore sorting system revealed from global classification of ocean litter

2021 ◽  
Vol 4 (6) ◽  
pp. 484-493
Author(s):  
Carmen Morales-Caselles ◽  
Josué Viejo ◽  
Elisa Martí ◽  
Daniel González-Fernández ◽  
Hannah Pragnell-Raasch ◽  
...  
Keyword(s):  
Global Classification ◽  
Sorting System

Toward a global classification of mast cell activation diseases

2011 ◽  
Vol 127 (5) ◽  
pp. 1311 ◽  
Author(s):  
Gerhard J. Molderings ◽  
Jürgen Homann ◽  
Martin Raithel ◽  
Thomas Frieling
Keyword(s):  
Mast Cell ◽  
Cell Activation ◽  
Mast Cell Activation ◽  
Global Classification

scholarly journals Cultural Issues Related to ICD-11 Mental, Behavioural and Neurodevelopmental Disorders

10.17816/cp67 ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 7-15
Author(s):  
Pratap Sharan ◽  
Gagan Hans
Keyword(s):  
Neurodevelopmental Disorders ◽  
Cultural Issues ◽  
Cultural Variations ◽  
Cultural Challenges ◽  
Global Classification ◽  
Specific Disorders ◽  
Successive Generations ◽  
Classificatory System

The challenge of producing a classificatory system that is truly representative of different regions and cultural variations is difficult. This can be conceptualized as an ongoing process, achievable by constant commitment in this regard from various stakeholders over successive generations of the classificatory systems. The objective of this article is to conduct a qualitative review of the process and outcome of the efforts that resulted in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders becoming a global classification. The ICD-11 represents an important, albeit iterative, advance in the classification of mental, behavioural and neurodevelopmental disorders. Significant changes have been incorporated in this regard, such as the introduction of new, culturally-relevant categories, modifications of the diagnostic guidelines, based on culturally informed data and the incorporation of culture-related features for specific disorders. Notwithstanding, there are still certain significant shortcomings and areas for further improvement and research. Some of the key limitations of ICD-11 relate to the paucity of research on the role of culture in the pathogenesis of illnesses. To ensure a classificatory system that is fair, reliable and culturally useful, there is a need to generate empirical evidence on diversity in the form of illnesses, as well as mechanisms that explain these in all the regions of the world. In this review, we try to delineate the various cultural challenges and their influences in the formulation of ICD-11, along with potential shortcomings and areas in need of more improvement and research in this regard.

A global classification of solar radiation

Solar Energy ◽  
1970 ◽  
Vol 13 (1) ◽  
pp. 67-81 ◽  
Author(s):  
W.H. Terjung
Keyword(s):  
Solar Radiation ◽  
Global Classification

A neural network architecture for data classification

2001 ◽  
Vol 11 (01) ◽  
pp. 33-42 ◽  
Author(s):  
Olivier Lezoray ◽  
Hubert Cardot
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Classification Rate ◽  
Improved Performance ◽  
Global Classification ◽  
Hidden Neurons ◽  
Number Of Classes

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

scholarly journals Fishing With (Proto)Net—A Principled Approach to Protein Target Selection

2003 ◽  
Vol 4 (5) ◽  
pp. 542-548
Author(s):  
Michal Linial
Keyword(s):  
Structural Genomics ◽  
Target Selection ◽  
Structural Diversity ◽  
Structural Data ◽  
Agglomerative Clustering ◽  
Homologous Proteins ◽  
Hierarchical Agglomerative Clustering ◽  
Global Classification ◽  
Protein Space

Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have not been determined, but that represent an as yet unknown structural superfamily or fold. Once such a structure is solved, it can be used as a template for modelling homologous proteins. This will aid in unveiling the structural diversity of the protein space. Currently, no reliable method for accurate 3D structural prediction is available when a sequence or a structure homologue is not available. Here we present a systematic methodology for selecting target proteins whose structure is likely to adopt a new, as yet unknown superfamily or fold. Our method takes advantage of a global classification of the sequence space as presented by ProtoNet-3D, which is a hierarchical agglomerative clustering of the proteins of interest (the proteins in Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet structurally solved, along with the probability of each of the proteins belonging to a new superfamily or fold. The sorted list has been self-validated against real structural data that was not available when the predictions were made. The practical application of using our computational–statistical method to determine novel superfamilies for structural genomics projects is also discussed.

scholarly journals Viral Taxonomy Derived From Evolutionary Genome Relationships

2018 ◽  
Author(s):  
Tyler J. Dougan ◽  
Stephen R. Quake
Keyword(s):  
Information Theory ◽  
Genetic Relationships ◽  
Computational Cost ◽  
Pairwise Comparisons ◽  
Genome Alignment ◽  
Evolutionary Genetic ◽  
Novel Virus ◽  
Global Classification ◽  
Ncbi Blast

AbstractWe describe a new genome alignment-based model for classification of viruses based on evolutionary genetic relationships. This approach uses information theory and a physical model to determine the information shared by the genes in two genomes. Pairwise comparisons of genes from the viruses are created from alignments using NCBI BLAST, and their match scores are combined to produce a metric between genomes, which is in turn used to determine a global classification using the 5,817 viruses on RefSeq. In cases where there is no measurable alignment between any genes, the method falls back to a coarser measure of genome relationship: the mutual information of k-mer frequency. This results in a principled model which depends only on the genome sequence, which captures many interesting relationships between viral families, and which creates clusters which correlate well with both the Baltimore and ICTV classifications. The incremental computational cost of classifying a novel virus is low and therefore newly discovered viruses can be quickly identified and classified.

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