Zhang Mengying, Su Qiang, Lu Yi, Zhao Manman, Niu Bing
College of Life Science, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China.
Med Chem. 2017;13(6):506-514. doi: 10.2174/1573406413666170522150940.
Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics.
In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed.
SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods.
This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress.
蛋白质组学致力于研究蛋白质的结构、功能及相互作用。蛋白质-蛋白质相互作用（PPI）的信息有助于增进我们对蛋白质功能及三维结构的了解。因此，确定PPI对于蛋白质组学研究至关重要。
在本综述中，为研究机器学习在预测PPI中的应用，选取了一些机器学习方法，如支持向量机（SVM）、人工神经网络（ANN）和随机森林（RF），并列举了其在PPI中的应用实例。
SVM和RF是两种常用方法。如今，更多研究人员通过结合两种以上方法来预测PPI。
本综述介绍了机器学习方法在预测PPI中的应用。讨论了PPI预测领域中许多成功的识别和预测实例，且PPI研究仍在进行中。
