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

机译:基于计算网格的生物信息学智能ART1分类系统设计

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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:计算网格技术已被视为解决大规模生物信息学相关问题并提高具有分布式bioDA TA集的多个计算平台上的数据准确性和处理速度的问题。本文提出了一种基于网格的智能ART1分类器,该分类器使用具有分布式GPCR数据集的Grid计算资源来进行ART1聚类分类。与基于Grid的ART1分类器和基于Grid的3层ART1分类器相比,本文评估了基于Grid的ART1分类器的性能。基于网格的智能ART1分类器的通信消息数少于其他分类器。而基于网格的智能ART1分类器的分类处理时间是基于网格的ART1分类器的13%的分类处理时间,是基于网格的3层ART1分类器的25%的分类处理时间。使用大规模且地理分布的bioDATA集进行高性能处理的广阔前景,并在保证合理的处理资源下确保高bioData准确性方面取得了良好进展。

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