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By introducing the \u2018CNN-BiLSTM-SVM classifier\u2019 and \u2018cellular automata image\u2019, a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has \u226590% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http:\/\/www.jci-bioinfo.cn\/ iAMP-CA2L, and the predictor program has been uploaded to https:\/\/github.com\/liujin66\/iAMP-CA2L.<\/jats:p>","DOI":"10.1093\/bib\/bbab209","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T11:21:18Z","timestamp":1620818478000},"source":"Crossref","is-referenced-by-count":106,"title":["iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types"],"prefix":"10.1093","volume":"22","author":[{"given":"Xuan","family":"Xiao","sequence":"first","affiliation":[{"name":"Jing-De-Zhen Ceramic Institute, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Tao","family":"Shao","sequence":"additional","affiliation":[{"name":"Jing-De-Zhen Ceramic 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