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AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.<\/jats:p>","DOI":"10.1093\/bib\/bbab083","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T12:14:31Z","timestamp":1614082471000},"source":"Crossref","is-referenced-by-count":146,"title":["Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides"],"prefix":"10.1093","volume":"22","author":[{"given":"Jing","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5216-3213","authenticated-orcid":false,"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, the Peter Doherty Institute for Infection and Immunity, the University of Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Leier","sequence":"additional","affiliation":[{"name":"Department of Genetics, UAB School of Medicine, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxu","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsin-Hui","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Biochemistry & Molecular Biology and Department of Materials Science & Engineering, Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatiana T","family":"Marquez Lago","sequence":"additional","affiliation":[{"name":"Departments of Genetics and Microbiology, UAB School of Medicine, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-8053","authenticated-orcid":false,"given":"Dong-Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute, Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"2021090817423985500_ref1","doi-asserted-by":"crossref","first-page":"D586","DOI":"10.1093\/nar\/gkh032","article-title":"ANTIMIC: a database of antimicrobial sequences","volume":"32","author":"Brahmachary","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2021090817423985500_ref2","doi-asserted-by":"crossref","first-page":"D265","DOI":"10.1093\/nar\/gkl866","article-title":"Defensins knowledgebase: a manually curated database and information source focused on the defensins family of antimicrobial 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