Review of Machine Learning Prediction Algorithms for Antimicrobial Peptides

The traditional methods for the identification of antimicrobial peptides are experimental means, which is inefficient and consumes a lot of manpower and material resources. The latest ways to identify antimicrobial peptides combine computer technology, bioinformatics, and machine learning methods together. Based on big data mining and analysis, antimicrobial peptides can be predicted from a large amount of peptide sequence data. The identification of antimicrobial peptides thereby could be accelerated. This paper classifies and analyzes the main literatures of the computer-aided antimicrobial peptide recognition in the recent 10 years, sorts out the main antimicrobial peptides data resources, the characteristic engineering of antimicrobial peptide recognitions, the machine learning prediction algorithms of antimicrobial peptides, the regression analysis methods of antimicrobial peptides. In the meanwhile, this paper reviews the model performance evaluation methods of machine learning algorithms, summarizes the existing shortcomings, and prospects the future development directions.