LAUSR

With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein–DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein–DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence‐digitizing representation strategies used in different types of computational methods are also summarized and discussed.