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Design of Computational Grid-based Intelligence ART1 Classification System for Bioinformatics Applications

机译:基于计算网格的智能艺术艺术艺术艺术艺术艺术艺术系统的设计

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Computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDA TA sets. This paper proposes a Gridbased intelligence ART1 classifier which operates an ART1 clustering classification using Grid computational resources with distributed GPCR data sets. This paper evaluates performance of the Gridbased ART1 classifier in comparing to the Grid-based ART1 classifier and the Grid-based 3-tier ART1 classifier. The number of communication message of the Grid-based intelligence ART1 classifier is less than the others classifier. And the classification processing time of the Grid-based intelligence ART1 classifier is 13% classification processing time of the Grid-based ART1 classifier and is 25% classification processing time of the Grid-based 3-tier ART1 classifier.Computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets and provides a good progress in guaranteeing high bioData accuracy with reasonable processing resources.
机译:已经注意到计算网格技术作为解决大规模生物信息化相关问题的问题,并提高了具有分布式BIODA TA套件的多个计算平台上的数据准确性和处理速度。本文提出了一种基于网格化的智能ART1分类器,其使用具有分布式GPCR数据集的网格计算资源操作ART1聚类分类。本文评估了Grid基于ART1分类器的性能与基于网格的ART1分类器和基于网格的3层ART1分类器相比。基于网格的智能ART1分类器的通信消息的数量小于其他分类器。基于网格的智能ART1分类器的分类处理时间是基于网格的ART1分类器的13%分类处理时间,并且是基于网格的3层ART1分类器的25%分类处理时间,从而在生物信息学应用中归属化网格具有大规模和地理上分布的BIODATA的高性能处理的高度希望,并提供了通过合理的处理资源保证高生物数据准确性的良好进展。

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