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PREDICTING THE FUNCTION OF PROTEINS USING DIFFERENTIAL EVOLUTION

机译:预测蛋白质使用差分演变的功能

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The proteins perform major of the most important tasks in the organism of any live being. Since the number of functions performed by them is large and there are relationships of dependency among them, knowing their function is an important challenge in bioinformatics. Thus, some methods of the Artificial Intelligence have been used for the hierarchical classification of proteins. In this work the RCMDE (Rule Construction Method using Differential Evolution) algorithm is proposed. The main objective of RCMDE is to construct a single label local hierarchical classifier using Differential Evolution. RCMDE is compared with LMNBwU, a hierarchical classifier based on Naive Bayes algorithm. RCMDE have shown good capacity of exploration of the space state and to construct good rules. In the experiments, using the enzymes datasets, RCMDE outperformed or had similar results with LMNBwU in all the experiments.
机译:蛋白质在任何现场存在的生物体中表现出最重要的任务。由于它们执行的函数数量很大并且它们之间存在依赖关系,因此知道其功能是生物信息学中的重要挑战。因此,人工智能的一些方法已被用于蛋白质的分层分类。在这项工作中,提出了RCMDE(使用差分演化的规则施工方法)算法。 RCMDE的主要目标是使用差分演进来构建单个标签本地分层分类器。 RCMDE与LMNBWU相比,基于Naive Bayes算法的分层分类器。 RCMDE表现出良好的空间状态探索能力,并构建良好规则。在实验中,使用酶数据集,RCMDE在所有实验中与LMNBWU相似或与LMNBWU相似的结果。

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