In silico prediction methods of self-interacting proteins: an empirical and academic survey

In silico prediction of self-interacting proteins (SIPs) has become an important part of proteomics. There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments. The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction, to provide important references for actual work in the future. In this review, we first describe the data required for the task of DTIs prediction. Then, some interesting feature extraction methods and computational models are presented on this topic in a timely manner. Afterwards, an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes. Overall, we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

Automated summary

This review provides a comprehensive overview of recent literature on computational prediction of self-interacting proteins (SIPs), serving as a valuable reference for future work. The review first describes the data required for predicting drug-target interactions (DTIs), followed by the presentation of interesting feature extraction methods and computational models. An empirical comparison is then conducted to demonstrate the prediction performance of various classifiers under different feature extraction and encoding schemes. Overall, potential methods for further enhancing SIPs prediction performance and related research directions are summarized and highlighted.