Tang Tao, Zhang Xiaocai, Liu Yuansheng, Peng Hui, Zheng Binshuang, Yin Yanlin, Zeng Xiangxiang
School of Mordern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Qixia District, 210023 Jiangsu, China.
College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086 Changsha, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad076.
Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.
蛋白质-蛋白质相互作用（PPIs）执行所有生物体的细胞过程。用于检测PPIs的实验方法存在成本高和假阳性率高的问题，因此高效的计算方法对于促进PPIs检测非常必要。近年来，受益于先进的高通量技术产生的大量蛋白质数据，机器学习模型在PPIs预测领域得到了很好的发展。在本文中，我们对最近提出的基于机器学习的预测方法进行了全面综述。还概述了这些方法中应用的机器学习模型以及蛋白质数据表示的细节。为了了解PPIs预测的潜在改进，我们讨论了基于机器学习的方法的发展趋势。最后，我们强调了PPIs预测的潜在方向，例如使用计算预测的蛋白质结构来扩展机器学习模型的数据源。这篇综述旨在为该领域的进一步改进提供参考。
