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Furthermore, in terms of protein identification, Xu et al. concentrated on the study of antioxidant protein identification; they proposed a machine learning method, SeqSVM, to predict antioxidant proteins through extracted sequence features [[10]]. The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases. Seven papers focus on describing protein function prediction or protein identification, which include the prediction of signal peptides in proteins, protein hydroxylation site prediction, protein-protein interaction (PPI) prediction, and protein identification. [Extracted from the article]

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section. [ABSTRACT FROM AUTHOR]