Recent Advances in Bioinformatics for Protein Function Prediction

With the continuous enhancement of computing power and the rapid expansion of biological data, bioinformatics has gradually emerged as a mainstream approach to solving biological problems. In biological research, protein function prediction is a crucial task that can significantly reduce research costs and improve efficiency. Therefore, the use of bioinformatics for protein function prediction has become a cutting-edge research focus. This article categorizes the application methods of bioinformatics in protein function prediction into three types: prediction methods based on protein sequences, prediction methods based on protein structures, and prediction methods based on protein interaction networks. It also provides a detailed analysis of key algorithms such as DeepGOPlus, MultiPredGO, and DeepGraphGO, highlighting their strengths, limitations, and recent progress. By offering an overview of state-of-the-art methods, this paper aims to serve as a comprehensive reference for advancing protein function prediction and exploring its implications in life sciences.