Zhang Shao-Wu, Wei Ze-Gang
College of Automation, Northwestern Polytechnical University, 710072, Xi'an, China, and Key Laboratory of Information Fusion Technology, Ministry of Education, 710072, Xi'an, China.
Med Chem. 2015;11(3):254-64. doi: 10.2174/1573406411666141230095838.
Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics.
蛋白质-蛋白质相互作用（PPIs）在许多细胞过程中起着关键作用。揭示细胞内的PPIs及其功能是后基因组生物学面临的一项挑战，将增进我们对疾病的理解，并有助于开发疾病诊断和法医鉴定的新方法。目前用于识别PPIs的实验方法既耗时又昂贵，而且高通量实验结果显示，除了蛋白质相互作用的假阴性信息外，还存在较高的假阳性。通过开发预测PPIs的计算方法并验证所获得的实验结果，可以克服这些障碍。在这项工作中，我们将从以下几个方面描述预测蛋白质-蛋白质相互作用的最新进展：i）基准数据集构建，ii）序列表示方法，iii）常见的机器学习算法，以及iv）交叉验证测试方法和评估指标。
