论文标题
使用集合学习算法的抗菌肽预测
Antimicrobial Peptide Prediction Using Ensemble Learning Algorithm
论文作者
论文摘要
最近，作为对细菌的第一道防线，抗微生物肽（AMP）一直是研究的一个感兴趣的领域。他们将注意力作为抵抗多药抵抗的有效方式。在湿实验室中发现和识别AMP是具有挑战性，昂贵且耗时的。因此，使用计算方法进行AMP预测，因为它们是更有效的方法，因此引起了人们的注意。在本文中，我们开发了一种有希望的集合学习算法，该算法将众所周知的学习模型整合在一起以预测AMP。首先，我们从肽序列的物理化学，进化和二级结构特性中提取了最佳特征。然后，我们的集合算法使用常规算法训练数据。最后，与传统学习算法相比，提出的合奏算法提高了预测的性能。
Recently, Antimicrobial peptides (AMPs) have been an area of interest in the researches, as the first line of defense against the bacteria. They are raising attention as an efficient way of fighting multidrug resistance. Discovering and identification of AMPs in the wet labs are challenging, expensive, and time-consuming. Therefore, using computational methods for AMP predictions have grown attention as they are more efficient approaches. In this paper, we developed a promising ensemble learning algorithm that integrates well-known learning models to predict AMPs. First, we extracted the optimal features from the physicochemical, evolutionary, and secondary structure properties of the peptide sequences. Our ensemble algorithm then trains the data using conventional algorithms. Finally, the proposed ensemble algorithm has improved the performance of the prediction by about 10% comparing to the traditional learning algorithms
