Protein-Protein Interaction Prediction with Deep Learning: A Survey

The Hong Kong University of Science and Technology (Guangzhou)

Data Science and Analytics Thrust

PhD Qualifying Examination

By Mr. Ziqi GAO

Abstract

Protein-protein interactions (PPIs) are essential for the cellular processes of all living organisms. However, experimental methods for detecting PPIs are often expensive and prone to false positives. Therefore, there is a high demand for efficient computational methods to facilitate PPI detection. With the increasing amount of protein data generated by advanced high-throughput technologies, machine learning models have become a promising approach for PPI prediction. In this review, we provide a comprehensive overview of the latest machine learning-based prediction methods for PPIs. Specifically, we comprehensively considered three tasks highly related to PPI: binary PPI prediction and protein-protein docking. We outline the machine learning models used in these two tasks and discuss the protein data representation in detail. To shed light on the future of PPI prediction, we analyze the trends in the development of machine learning-based methods. We also suggest potential directions for PPI prediction, such as incorporating computationally predicted protein structures to expand the data source for machine learning models. Our review aims to serve as a helpful guide for researchers seeking to improve PPI prediction in this field.

PQE Committee

Chairperson: Prof. Nan TANG

Prime Supervisor: Prof Jia LI

Co-Supervisor: Prof Yong HUANG

Examiner: Prof Yanlin ZHANG