利用神经网络集合预测革兰氏阴性菌的蛋白质亚细胞位置

Junwei Ma, Wenqi Liu, Hong Gu
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引用次数: 4

摘要

革兰氏阴性菌的许多种类都是致病菌,可以引起宿主生物的疾病。这种致病能力通常与革兰氏阴性细胞中的某些成分有关,因此开发一种预测革兰氏阴性细菌蛋白亚细胞位置的有效方法是非常必要的。为了反映神经网络在该领域的广泛应用,我们基于Elman网络设计了7种不同的训练函数,并使用遗传算法选择合适的网络进行集成。实验结果表明,神经网络集成在性能上具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we design seven different training functions based on Elman networks, and use a genetic algorithm to select the proper networks for an ensemble. Experimental results show that the neural networks ensemble has a dominant advantage in performance.
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