Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs.

中文翻译：

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Ensemble-AMPPred：使用集成学习方法进行稳健的 AMP 预测和识别，该方法具有用于区分 AMP 的新混合特征

抗菌肽（AMP）是具有抗菌活性的天然肽。这些肽是先天免疫系统的重要组成部分。它们存在于各种生物体中。通过实验技术筛选和鉴定 AMP 是一项费时费力的任务。或者，已经开发了基于机器学习的计算方法来在实验验证之前筛选潜在的 AMP 候选者。尽管有各种 AMP 预测程序可用，但仍需要改进以减少误报 (FP) 并提高预测准确性。在这项工作中，基于平衡的训练数据集和两个大型测试数据集，探索和评估了几种著名的单一和集成机器学习方法。我们已经证明，具有各种预测模型的开发程序在区分 AMP 和非 AMP 方面具有很高的性能。因此，我们描述了使用 MaxProbVote 预测和识别 AMP 的程序的开发，这是一个集成模型。此外，为了提高预测效率，集成模型与基于逻辑回归的新混合特征相结合。集成了混合特征的集成模型可以有效地提高名为 Ensemble-AMPPred 的开发程序的预测灵敏度，与当前可用程序相比，在灵敏度和特异性方面都得到了整体改进。我们描述了使用 MaxProbVote 预测和识别 AMP 的程序的开发，这是一个集成模型。此外，为了提高预测效率，集成模型与基于逻辑回归的新混合特征相结合。集成了混合特征的集成模型可以有效地提高名为 Ensemble-AMPPred 的开发程序的预测灵敏度，与当前可用程序相比，在灵敏度和特异性方面都得到了整体改进。我们描述了使用 MaxProbVote 预测和识别 AMP 的程序的开发，这是一个集成模型。此外，为了提高预测效率，集成模型与基于逻辑回归的新混合特征相结合。集成了混合特征的集成模型可以有效地提高名为 Ensemble-AMPPred 的开发程序的预测灵敏度，与当前可用程序相比，在灵敏度和特异性方面都得到了整体改进。