分类器(UML)
人工神经网络
计算机科学
人工智能
机器学习
鉴定(生物学)
一般化
支持向量机
药物开发
计算生物学
药品
生物
数学
数学分析
植物
药理学
作者
Ruiquan Ge,Yixiao Xia,Minchao Jiang,Gangyong Jia,Xiaoyang Jing,Ye Li,Yunpeng Cai
标识
DOI:10.1101/2022.06.10.495721
摘要
Abstract Motivation The virus poses a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, accurate and rapid identification of antiviral peptides using computational methods is critical. However, it is still challenging to extract effective feature representations from the sequences for the accurate identification of antiviral peptides. Results This study introduces a novel two-step approach, named HybAVPnet, with a hybrid network architecture to identify antiviral peptides based on neural networks and traditional machine learning methods. Firstly, eighteen kinds of features are extracted to predict labels and probabilities by the neural network classifier and LightGBM classifier, respectively. Secondly, the support vector machine classifier is carried out using the predicted probability of the first step to make the final prediction. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, especially on independent datasets, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability. Availability and implementation The predicted model could be downloaded from: https://github.com/greyspring/HybAVPnet Contact gespring@hdu.edu.cn ; yp.cai@siat.ac.cn Supplementary information Supplementary data are available at Bioinformatics online.
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