Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan.
Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae305.
Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted.
In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities.
尽管抗病毒药物和疫苗的制造已经非常广泛，但病毒感染仍然是人类的主要疾病。抗病毒肽 (AVP) 作为新型抗病毒药物的潜在候选药物已经出现。这些肽通过针对病毒生命周期的不同阶段，对多种病毒表现出强烈的抗病毒活性。因此，准确预测 AVP 是一项必不可少但具有挑战性的任务。最近，已经开发了许多基于机器学习的方法来实现这一目标；然而，由于其在特征工程、准确性和泛化能力方面的局限性，这些方法受到限制。
在本研究中，我们旨在开发一种有效的基于机器学习的 AVP 识别方法，称为 DeepAVP-TPPred，以解决上述问题。首先，我们使用我们设计的基于图像的特征提取算法提取两个新的转换特征集，并将它们与基于进化信息的特征集成。接下来，使用一种称为二叉树增长算法的新特征选择方法对这些特征集进行优化。最后，将来自训练数据集的最佳特征空间输入到深度神经网络中以构建最终的分类模型。所提出的模型 DeepAVP-TPPred 通过严格的 5 折交叉验证和两种独立的数据集测试方法进行了测试，与现有预测器相比，在准确性和泛化能力方面均表现出了最佳性能和增强的效率。
