{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T01:23:13Z","timestamp":1793150593085,"version":"4.5.0"},"reference-count":220,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"publisher","award":["MYRG2019-00098-FST"],"award-info":[{"award-number":["MYRG2019-00098-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.<\/jats:p>","DOI":"10.3390\/antibiotics11101451","type":"journal-article","created":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T20:43:50Z","timestamp":1666557830000},"page":"1451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Jielu","family":"Yan","sequence":"first","affiliation":[{"name":"PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiu","family":"Cai","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"},{"name":"Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2497-9519","authenticated-orcid":false,"given":"Bob","family":"Zhang","sequence":"additional","affiliation":[{"name":"PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-5091","authenticated-orcid":false,"given":"Yapeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek F.","family":"Wong","sequence":"additional","affiliation":[{"name":"NLP2CT Lab, Department of Computer and Information Science, University of Macau, Taipa, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3695-7758","authenticated-orcid":false,"given":"Shirley W. I.","family":"Siu","sequence":"additional","affiliation":[{"name":"Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China"},{"name":"School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/s41573-019-0058-8","article-title":"Antimicrobial host defence peptides: Functions and clinical potential","volume":"19","author":"Mookherjee","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.2174\/138161209788682325","article-title":"The Roles of Antimicrobial Peptides in Innate Host Defense","volume":"15","author":"Diamond","year":"2009","journal-title":"Curr. Pharm. Des."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4538","DOI":"10.1038\/s41467-019-12364-6","article-title":"Integrated evolutionary analysis reveals antimicrobial peptides with limited resistance","volume":"10","author":"Spohn","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.omto.2019.12.001","article-title":"In-Vitro and MD Simulation Study to Explore Physicochemical Parameters for Antibacterial Peptide to Become Potent Anticancer Peptide","volume":"16","author":"Ma","year":"2020","journal-title":"Mol. Ther.-Oncolytics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"D1087","DOI":"10.1093\/nar\/gkv1278","article-title":"APD3: The antimicrobial peptide database as a tool for research and education","volume":"44","author":"Wang","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"D460","DOI":"10.1093\/nar\/gkab1080","article-title":"dbAMP 2.0: Updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data","volume":"50","author":"Jhong","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"D288","DOI":"10.1093\/nar\/gkaa991","article-title":"DBAASP v3: Database of antimicrobial\/cytotoxic activity and structure of peptides as a resource for development of new therapeutics","volume":"49","author":"Pirtskhalava","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7820","DOI":"10.1039\/D0CS00729C","article-title":"The multifaceted nature of antimicrobial peptides: Current synthetic chemistry approaches and future directions","volume":"50","author":"Gan","year":"2021","journal-title":"Chem. Soc. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1038\/jid.2011.387","article-title":"Antimicrobial Peptides: Old Molecules with New Ideas","volume":"132","author":"Nakatsuji","year":"2012","journal-title":"J. Investig. Dermatol."},{"key":"ref_10","first-page":"306","article-title":"On a remarkable bacteriolytic element found in tissues and secretions","volume":"93","author":"Fleming","year":"1922","journal-title":"Proc. R. Soc. Lond. Ser. B Contain. Pap. A Biol. Character"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ragland, S.A., and Criss, A.K. (2017). From bacterial killing to immune modulation: Recent insights into the functions of lysozyme. PLoS Pathog., 13.","DOI":"10.1371\/journal.ppat.1006512"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1128\/jb.16.4.211-229.1928","article-title":"Limiting Factors in the Lactic Fermentation","volume":"16","author":"Rogers","year":"1928","journal-title":"J. Bacteriol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1111\/jam.13033","article-title":"Biomedical applications of nisin","volume":"120","author":"Shin","year":"2016","journal-title":"J. Appl. Microbiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1093\/jac\/41.3.341","article-title":"Antibacterial efficacy of nisin against multidrug-resistant Gram-positive pathogens","volume":"41","author":"Severina","year":"1998","journal-title":"J. Antimicrob. Chemother."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1084\/jem.70.1.11","article-title":"Studies on a Bactericidal Agent Extracted from a Soil Bacillus: II. Protective Effect of the Bactericidal Agent against Experimental Pneumococcus Infections in Mice","volume":"70","author":"Dubos","year":"1939","journal-title":"J. Exp. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1016\/j.bbamem.2009.01.004","article-title":"Bombinins, antimicrobial peptides from Bombina species","volume":"1788","author":"Simmaco","year":"2009","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1126\/science.142.3595.1085","article-title":"Antibacterial and Enzymic Basic Proteins from Leukocyte Lysosomes: Separation and Identification","volume":"142","author":"Zeya","year":"1963","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1172\/JCI112120","article-title":"Defensins. Natural peptide antibiotics of human neutrophils","volume":"76","author":"Ganz","year":"1985","journal-title":"J. Clin. Investig."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/j.1432-1033.1980.tb05991.x","article-title":"Insect Immunity. Purification and Properties of Three Inducible Bactericidal Proteins from Hemolymph of Immunized Pupae of Hyalophora Cecropia","volume":"106","author":"Hultmark","year":"1980","journal-title":"Eur. J. Biochem."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1073\/pnas.84.15.5449","article-title":"Magainins, a class of antimicrobial peptides from Xenopus skin: Isolation, characterization of two active forms, and partial cDNA sequence of a precursor","volume":"84","author":"Zasloff","year":"1987","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1074\/jbc.273.4.2059","article-title":"Lycotoxins, Antimicrobial Peptides from Venom of the Wolf SpiderLycosa carolinensis","volume":"273","author":"Yan","year":"1998","journal-title":"J. Biol. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s12223-013-0280-4","article-title":"Plant antimicrobial peptides","volume":"59","author":"Nawrot","year":"2014","journal-title":"Folia Microbiol."},{"key":"ref_23","first-page":"279","article-title":"A Crystalline Protein Obtained from a Lipoprotein of Wheat Flour","volume":"19","author":"Balls","year":"1942","journal-title":"Cereal Chem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0014-5793(90)81265-P","article-title":"\u03b3-Purothionins: Amino acid sequence of two polypeptides of a new family of thionins from wheat endosperm","volume":"270","author":"Colilla","year":"1990","journal-title":"FEBS Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1104\/pp.108.4.1353","article-title":"Plant Defensins: Nove1 Antimicrobial Peptides as Components of the Host Defense System","volume":"108","author":"Broekaert","year":"1995","journal-title":"Plant Physiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.mie.2021.09.006","article-title":"Chapter One\u2014Unifying the classification of antimicrobial peptides in the antimicrobial peptide database","volume":"Volume 663","author":"Hicks","year":"2022","journal-title":"Methods in Enzymology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.tips.2019.04.012","article-title":"The Vast Structural Diversity of Antimicrobial Peptides","volume":"40","author":"Koehbach","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"39","DOI":"10.56499\/jppres13.005_1.2.39","article-title":"Antimicrobial peptides: The role of hydrophobicity in the alpha helical structure","volume":"1","author":"Perumal","year":"2013","journal-title":"J. Pharm. Pharmacogn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.bbamem.2015.11.003","article-title":"The human cathelicidin LL-37\u2014A pore-forming antibacterial peptide and host-cell modulator","volume":"1858","author":"Xhindoli","year":"2016","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1038\/292246a0","article-title":"Sequence and specificity of two antibacterial proteins involved in insect immunity","volume":"292","author":"Steiner","year":"1981","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1126\/science.177.4046.314","article-title":"Bee and Wasp Venoms: The biochemistry and pharmacology of their peptides and enzymes are reviewed","volume":"177","author":"Habermann","year":"1972","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0005-2736(99)00200-X","article-title":"Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by \u03b1-helical antimicrobial and cell non-selective membrane-lytic peptides","volume":"1462","author":"Shai","year":"1999","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pino-Angeles, A., Leveritt, J.M., and Lazaridis, T. (2016). Pore Structure and Synergy in Antimicrobial Peptides of the Magainin Family. PLoS Comput. Biol., 12.","DOI":"10.1371\/journal.pcbi.1004570"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3392","DOI":"10.1529\/biophysj.104.056424","article-title":"Concentration-Dependent Realignment of the Antimicrobial Peptide PGLa in Lipid Membranes Observed by Solid-State 19F-NMR","volume":"88","author":"Glaser","year":"2005","journal-title":"Biophys. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1021\/acsinfecdis.6b00045","article-title":"Contribution of Amphipathicity and Hydrophobicity to the Antimicrobial Activity and Cytotoxicity of \u03b2-Hairpin Peptides","volume":"2","author":"Edwards","year":"2016","journal-title":"ACS Infect. Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.32607\/20758251-2015-7-1-37-47","article-title":"Structure and Biological Functions of \u03b2-Hairpin Antimicrobial Peptides","volume":"7","author":"Panteleev","year":"2015","journal-title":"Acta Nat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10612","DOI":"10.1002\/anie.201402167","article-title":"The Chemistry and Biology of Theta Defensins","volume":"53","author":"Conibear","year":"2014","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1126\/science.286.5439.498","article-title":"A Cyclic Antimicrobial Peptide Produced in Primate Leukocytes by the Ligation of Two Truncated \u03b1-Defensins","volume":"286","author":"Tang","year":"1999","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1002\/cbic.201300568","article-title":"The Cyclic Cystine Ladder of Theta-Defensins as a Stable, Bifunctional Scaffold: A Proof-of-Concept Study Using the Integrin-Binding RGD Motif","volume":"15","author":"Conibear","year":"2014","journal-title":"ChemBioChem"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Falanga, A., Nigro, E., De Biasi, M., Daniele, A., Morelli, G., Galdiero, S., and Scudiero, O. (2017). Cyclic Peptides as Novel Therapeutic Microbicides: Engineering of Human Defensin Mimetics. Molecules, 22.","DOI":"10.3390\/molecules22071217"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.bbamem.2006.07.007","article-title":"The human beta-defensin-3, an antibacterial peptide with multiple biological functions","volume":"1758","author":"Dhople","year":"2006","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/S0969-2126(01)00177-0","article-title":"Refined three-dimensional solution structure of insect defensin A","volume":"3","author":"Cornet","year":"1995","journal-title":"Structure"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.peptides.2015.04.017","article-title":"Cysteine-stabilized \u03b1\u03b2 defensins: From a common fold to antibacterial activity","volume":"72","author":"Dias","year":"2015","journal-title":"Peptides"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.bbamem.2006.04.006","article-title":"Tryptophan- and arginine-rich antimicrobial peptides: Structures and mechanisms of action","volume":"1758","author":"Chan","year":"2006","journal-title":"Biochim. Biophys. Acta (BBA)-Biomembr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15765","DOI":"10.1021\/bi000714m","article-title":"Structure of the Bovine Antimicrobial Peptide Indolicidin Bound to Dodecylphosphocholine and Sodium Dodecyl Sulfate Micelles","volume":"39","author":"Rozek","year":"2000","journal-title":"Biochemistry"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1093\/nar\/gki725","article-title":"Structural and DNA-binding studies on the bovine antimicrobial peptide, indolicidin: Evidence for multiple conformations involved in binding to membranes and DNA","volume":"33","author":"Hsu","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1038\/nrmicro1098","article-title":"Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria?","volume":"3","author":"Brogden","year":"2005","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.3389\/fimmu.2020.02077","article-title":"The Dual Role of Antimicrobial Peptides in Autoimmunity","volume":"11","author":"Liang","year":"2020","journal-title":"Front. Immunol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s10989-021-10325-6","article-title":"A Review of Antimicrobial Peptides: Its Function, Mode of Action and Therapeutic Potential","volume":"28","author":"Nayab","year":"2022","journal-title":"Int. J. Pept. Res. Ther."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1172\/JCI101251","article-title":"Experimental and Clinical Studies on Gramicidin 1","volume":"20","author":"Herrell","year":"1941","journal-title":"J. Clin. Investig."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1093\/infdis\/71.2.166","article-title":"Toxic Effects of Tyrothricin, Gramicidin and Tyrocidine","volume":"71","author":"Rammelkamp","year":"1942","journal-title":"J. Infect. Dis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1080\/10408398.2013.763765","article-title":"Nisin as a Food Preservative: Part 1: Physicochemical Properties, Antimicrobial Activity, and Main Uses","volume":"56","author":"Gharsallaoui","year":"2016","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"668632","DOI":"10.3389\/fcimb.2021.668632","article-title":"Antimicrobial Peptides: A New Hope in Biomedical and Pharmaceutical Fields","volume":"11","author":"Moretta","year":"2021","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s11274-009-9984-7","article-title":"Application of antimicrobial peptides in agriculture and food industry","volume":"25","author":"Keymanesh","year":"2009","journal-title":"World J. Microbiol. Biotechnol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"26790","DOI":"10.1038\/srep26790","article-title":"Use of recombinant porcine \u03b2-defensin 2 as a medicated feed additive for weaned piglets","volume":"6","author":"Peng","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huang, J., Yang, X., Wang, A., Huang, C., Tang, H., Zhang, Q., Fang, Q., Yu, Z., Liu, X., and Huang, Q. (2020). Pigs Overexpressing Porcine \u03b2-Defensin 2 Display Increased Resilience to Glaesserella parasuis Infection. Antibiotics, 9.","DOI":"10.3390\/antibiotics9120903"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.tifs.2021.04.019","article-title":"Antimicrobial peptides and their application in food packaging","volume":"112","author":"Liu","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1111\/j.1574-6968.2012.02528.x","article-title":"Resistance to antimicrobial peptides in Gram-negative bacteria","volume":"330","author":"Gruenheid","year":"2012","journal-title":"FEMS Microbiol. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2362","DOI":"10.3389\/fmicb.2020.593215","article-title":"Resistance Mechanisms to Antimicrobial Peptides in Gram-Positive Bacteria","volume":"11","author":"Assoni","year":"2020","journal-title":"Front. Microbiol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"16895","DOI":"10.18632\/oncotarget.7815","article-title":"iACP: A sequence-based tool for identifying anticancer peptides","volume":"7","author":"Chen","year":"2016","journal-title":"Oncotarget"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"276","DOI":"10.3389\/fphar.2018.00276","article-title":"AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front. Pharmacol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4007","DOI":"10.1093\/bioinformatics\/bty451","article-title":"ACPred-FL: A sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides","volume":"34","author":"Wei","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Torrent, M., Andreu, D., Nogu\u00e9s, V.M., and Boix, E. (2011). Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0016968"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"bbab439","DOI":"10.1093\/bib\/bbab439","article-title":"StaBle-ABPpred: A stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides","volume":"23","author":"Singh","year":"2022","journal-title":"Briefings Bioinform."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/JBHI.2020.2977091","article-title":"DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/prot.1035","article-title":"Prediction of protein cellular attributes using pseudo-amino acid composition","volume":"43","author":"Chou","year":"2001","journal-title":"Proteins Struct. Funct. Bioinform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.febslet.2004.09.036","article-title":"Reduced amino acid alphabet is sufficient to accurately recognize intrinsically disordered protein","volume":"576","author":"Weathers","year":"2004","journal-title":"FEBS Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1021\/acs.jcim.7b00414","article-title":"Recurrent Neural Network Model for Constructive Peptide Design","volume":"58","author":"Hiss","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1021\/acs.jcim.0c01441","article-title":"AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides","volume":"61","author":"Ferrell","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1038\/srep02984","article-title":"In silico models for designing and discovering novel anticancer peptides","volume":"3","author":"Tyagi","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.1021\/acs.jcim.1c00181","article-title":"xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning","volume":"61","author":"Chen","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chang, K.Y., Lin, T.P., Shih, L.Y., and Wang, C.K. (2015). Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0119490"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"347","DOI":"10.2174\/138620712799361807","article-title":"QSAR modeling and design of cationic antimicrobial peptides based on structural properties of amino acids","volume":"15","author":"Wang","year":"2012","journal-title":"Comb. Chem. High Throughput Screen."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sander, O., Sing, T., Sommer, I., Low, A.J., Cheung, P.K., Harrigan, P.R., Lengauer, T., and Domingues, F.S. (2007). Structural descriptors of gp120 V3 loop for the prediction of HIV-1 coreceptor usage. PLoS Comput. Biol., 3.","DOI":"10.1371\/journal.pcbi.0030058"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Minkiewicz, P., Iwaniak, A., and Darewicz, M. (2017). Annotation of Peptide Structures Using SMILES and Other Chemical Codes\u2013Practical Solutions. Mol. J. Synth. Chem. Nat. Prod. Chem., 22.","DOI":"10.3390\/molecules22122075"},{"key":"ref_76","first-page":"31","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Model."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"D837","DOI":"10.1093\/nar\/gku892","article-title":"CancerPPD: A database of anticancer peptides and proteins","volume":"43","author":"Tyagi","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"lqab039","DOI":"10.1093\/nargab\/lqab039","article-title":"A large-scale comparative study on peptide encodings for biomedical classification","volume":"3","author":"Mohsen","year":"2021","journal-title":"NAR Genom. Bioinform."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13040-019-0196-x","article-title":"Encodings and models for antimicrobial peptide classification for multi-resistant pathogens","volume":"12","author":"Heider","year":"2019","journal-title":"BioData Min."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","article-title":"Deep learning improves antimicrobial peptide recognition","volume":"34","author":"Veltri","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_81","unstructured":"Bengio, Y., and LeCun, Y. (2013, January 2\u20134). Efficient Estimation of Word Representations in Vector Space. Proceedings of the 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings, Scottsdale, AZ, USA."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"bbab065","DOI":"10.1093\/bib\/bbab065","article-title":"Deep-ABPpred: Identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec","volume":"22","author":"Sharma","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_83","unstructured":"Liu, Q., Kusner, M.J., and Blunsom, P. (2020). A Survey on Contextual Embeddings. arXiv."},{"key":"ref_84","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Curran Associates Inc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1016\/j.csbj.2021.03.022","article-title":"The language of proteins: NLP, machine learning & protein sequences","volume":"19","author":"Ofer","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"bbab200","DOI":"10.1093\/bib\/bbab200","article-title":"A novel antibacterial peptide recognition algorithm based on BERT","volume":"22","author":"Zhang","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_87","first-page":"89","article-title":"UniProtKB\/Swiss-Prot","volume":"406","author":"Boutet","year":"2007","journal-title":"Methods Mol. Biol."},{"key":"ref_88","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Human Language Technologies, Volume 1 (Long and Short Papers), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA, 2\u20137 June 2019, Association for Computational Linguistics."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"vbac021","DOI":"10.1093\/bioadv\/vbac021","article-title":"LMPred: Predicting antimicrobial peptides using pre-trained language models and deep learning","volume":"2","author":"Dee","year":"2022","journal-title":"Bioinform. Adv."},{"key":"ref_90","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P.J. (2019). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv."},{"key":"ref_91","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., and Le, Q.V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Porto, W.F., Pires, A.S., and Franco, O.L. (2012). CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0051444"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Porto, W.F., Fernandes, F.C., and Franco, O.L. (2010). An SVM Model Based on Physicochemical Properties to Predict Antimicrobial Activity from Protein Sequences with Cysteine Knot Motifs, Springer.","DOI":"10.1007\/978-3-642-15060-9_6"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"42362","DOI":"10.1038\/srep42362","article-title":"Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou\u2019s general PseAAC","volume":"7","author":"Meher","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4691","DOI":"10.1021\/acs.jcim.0c00841","article-title":"IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides","volume":"60","author":"Kavousi","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"bbab209","DOI":"10.1093\/bib\/bbab209","article-title":"iAMP-CA2L: A new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types","volume":"22","author":"Xiao","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"D774","DOI":"10.1093\/nar\/gkp1021","article-title":"CAMP: A useful resource for research on antimicrobial peptides","volume":"38","author":"Thomas","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1038\/s41598-018-19752-w","article-title":"AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest","volume":"8","author":"Bhadra","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Chung, C.R., Jhong, J.H., Wang, Z., Chen, S., Wan, Y., Horng, J.T., and Lee, T.Y. (2020). Characterization and identification of natural antimicrobial peptides on different organisms. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21030986"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"bbab083","DOI":"10.1093\/bib\/bbab083","article-title":"Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides","volume":"22","author":"Xu","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.1080\/07391102.2019.1695667","article-title":"Detecting antimicrobial peptides by exploring the mutual information of their sequences","volume":"38","author":"Tripathi","year":"2020","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"bbab242","DOI":"10.1093\/bib\/bbab242","article-title":"AniAMPpred: Artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom","volume":"22","author":"Sharma","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ab.2013.01.019","article-title":"iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types","volume":"436","author":"Xiao","year":"2013","journal-title":"Anal. Biochem."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"105577","DOI":"10.1016\/j.compbiomed.2022.105577","article-title":"AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning","volume":"146","author":"Lv","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Lertampaiporn, S., Vorapreeda, T., Hongsthong, A., and Thammarongtham, C. (2021). Ensemble-AMPPred: Robust AMP prediction and recognition using the ensemble learning method with a new hybrid feature for differentiating AMPs. Genes, 12.","DOI":"10.3390\/genes12020137"},{"key":"ref_106","unstructured":"Zarayeneh, N., and Hanifeloo, Z. (2020). Antimicrobial peptide prediction using ensemble learning algorithm. arXiv."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s12539-021-00435-6","article-title":"Using an ensemble to identify and classify macroalgae antimicrobial peptides","volume":"13","author":"Caprani","year":"2021","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"104516","DOI":"10.1016\/j.chemolab.2022.104516","article-title":"iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach","volume":"222","author":"Ahmad","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_109","first-page":"5","article-title":"Support vector machines for classification and regression","volume":"14","author":"Gunn","year":"1998","journal-title":"ISIS Tech. Rep."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0098-1354(03)00188-1","article-title":"Support vector classification with parameter tuning assisted by agent-based technique","volume":"28","author":"Kulkarni","year":"2004","journal-title":"Comput. Chem. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.ymeth.2019.04.008","article-title":"Deep Learning in Bioinformatics: Introduction, Application, and Perspective in Big Data Era","volume":"166","author":"Li","year":"2019","journal-title":"Methods"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1021\/jm8015365","article-title":"Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning","volume":"52","author":"Fjell","year":"2009","journal-title":"J. Med. Chem."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.omtn.2020.05.006","article-title":"Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning","volume":"20","author":"Yan","year":"2020","journal-title":"Mol. Ther.-Nucleic Acids"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1038\/s41587-022-01226-0","article-title":"Identification of antimicrobial peptides from the human gut microbiome using deep learning","volume":"40","author":"Ma","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Li, C., Sutherland, D., Hammond, S.A., Yang, C., Taho, F., Bergman, L., Houston, S., Warren, R.L., Wong, T., and Hoang, L.M.N. (2022). AMPlify: Attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genom., 23.","DOI":"10.1186\/s12864-022-08310-4"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Ruiz Puentes, P., Henao, M.C., Cifuentes, J., Mu\u00f1oz Camargo, C., Reyes, L.H., Cruz, J.C., and Arbel\u00e1ez, P. (2022). Rational discovery of antimicrobial peptides by means of artificial intelligence. Membranes, 12.","DOI":"10.3390\/membranes12070708"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"e00299-21","DOI":"10.1128\/mSystems.00299-21","article-title":"AI4AMP: An Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning","volume":"6","author":"Lin","year":"2021","journal-title":"mSystems"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"bbac094","DOI":"10.1093\/bib\/bbac094","article-title":"Do deep learning models make a difference in the identification of antimicrobial peptides?","volume":"23","author":"Brizuela","year":"2022","journal-title":"Briefings Bioinform."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"104214","DOI":"10.1016\/j.chemolab.2020.104214","article-title":"Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks","volume":"208","author":"Ahmad","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"111051","DOI":"10.1016\/j.biopha.2020.111051","article-title":"ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides","volume":"133","author":"Timmons","year":"2021","journal-title":"Biomed. Pharmacother."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1093\/bioinformatics\/btx285","article-title":"modlAMP: Python for antimicrobial peptides","volume":"33","author":"Gabernet","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"bbab258","DOI":"10.1093\/bib\/bbab258","article-title":"ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides","volume":"22","author":"Timmons","year":"2021","journal-title":"Briefings Bioinform."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/nar\/28.1.374","article-title":"AAindex: Amino acid index database","volume":"28","author":"Kawashima","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s12539-016-0151-1","article-title":"Enhanced prediction and characterization of CDK inhibitors using optimal class distribution","volume":"9","author":"Nath","year":"2017","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"103912","DOI":"10.1016\/j.chemolab.2019.103912","article-title":"cACP: Classifying anticancer peptides using discriminative intelligent model via Chou\u2019s 5-step rules and general pseudo components","volume":"196","author":"Akbar","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1007\/s00249-007-0188-5","article-title":"A reduced amino acid alphabet for understanding and designing protein adaptation to mutation","volume":"36","author":"Etchebest","year":"2007","journal-title":"Eur. Biophys. J."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_131","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","article-title":"Recent trends in deep learning based natural language processing","volume":"13","author":"Young","year":"2018","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_133","unstructured":"Dua, M., Barbara, D., and Shehu, A. (2020, January 23\u201325). Exploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition. Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, San Francisco, CA, USA."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Su, X., Xu, J., Yin, Y., Quan, X., and Zhang, H. (2019). Antimicrobial peptide identification using multi-scale convolutional network. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3327-y"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"e11906","DOI":"10.7717\/peerj.11906","article-title":"DLFF-ACP: Prediction of ACPs based on deep learning and multi-view features fusion","volume":"9","author":"Cao","year":"2021","journal-title":"PeerJ"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Sun, Y.Y., Lin, T.T., Cheng, W.C., Lu, I.H., Lin, C.Y., and Chen, S.H. (2022). Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning. Pharmaceuticals, 15.","DOI":"10.1101\/2022.02.01.478580"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.1109\/JBHI.2021.3130825","article-title":"Deep-AVPpred: Artificial intelligence driven discovery of peptide drugs for viral infections","volume":"26","author":"Sharma","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_139","first-page":"bpab021","article-title":"MultiPep: A hierarchical deep learning approach for multi-label classification of peptide bioactivities","volume":"6","author":"Kacprowski","year":"2021","journal-title":"Biol. Methods Protoc."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Li, S., Li, W., Cook, C., Zhu, C., and Gao, Y. (2018, January 17\u201319). Independently recurrent neural network (indrnn): Building a longer and deeper rnn. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2018.00572"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.omtn.2019.04.025","article-title":"ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation","volume":"17","author":"Yi","year":"2019","journal-title":"Mol. Ther.-Nucleic Acids"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1109\/TCBB.2019.2903800","article-title":"Classification of Antibacterial Peptides using Long Short-Term Memory Recurrent Neural Networks","volume":"17","author":"Youmans","year":"2019","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.omtn.2020.10.005","article-title":"DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm","volume":"22","author":"Yu","year":"2020","journal-title":"Mol. Ther.-Nucleic Acids"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1093\/bioinformatics\/bty937","article-title":"Identifying antimicrobial peptides using word embedding with deep recurrent neural networks","volume":"35","author":"Hamid","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"022002","DOI":"10.1088\/2516-1091\/ac5b13","article-title":"Known operator learning and hybrid machine learning in medical imaging\u2014A review of the past, the present, and the future","volume":"4","author":"Maier","year":"2022","journal-title":"Prog. Biomed. Eng."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Fu, H., Cao, Z., Li, M., and Wang, S. (2020). ACEP: Improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding. BMC Genom., 21.","DOI":"10.1186\/s12864-020-06978-0"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"21","DOI":"10.2197\/ipsjtbio.12.21","article-title":"Prediction of Antifungal Peptides by Deep Learning with Character Embedding","volume":"12","author":"Fang","year":"2019","journal-title":"IPSJ Trans. Bioinform."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"bbab422","DOI":"10.1093\/bib\/bbab422","article-title":"Deep-AFPpred: Identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM","volume":"23","author":"Sharma","year":"2022","journal-title":"Briefings Bioinform."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., and Rost, B. (2019). Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3220-8"},{"key":"ref_151","unstructured":"Bao, L., Lambert, P., and Badia, T. (August, January 28). Attention and lexicon regularized LSTM for aspect-based sentiment analysis. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, Italy."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12\u201317). Hierarchical attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/1097-0282(2000)55:1<4::AID-BIP30>3.0.CO;2-M","article-title":"Amphipathic, \u03b1-helical antimicrobial peptides","volume":"55","author":"Tossi","year":"2000","journal-title":"Pept. Sci."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"4946","DOI":"10.1093\/bioinformatics\/btz427","article-title":"ACME: Pan-specific peptide\u2013MHC class I binding prediction through attention-based deep neural networks","volume":"35","author":"Hu","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"508","DOI":"10.2174\/138920311796957720","article-title":"Cellular automata and its applications in protein bioinformatics","volume":"12","author":"Xiao","year":"2011","journal-title":"Curr. Protein Pept. Sci."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"bbab487","DOI":"10.1093\/bib\/bbab487","article-title":"sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks","volume":"23","author":"Hussain","year":"2022","journal-title":"Briefings Bioinform."},{"key":"ref_157","unstructured":"Bengio, Y., and LeCun, Y. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Salem, M., Arshadi, A.K., and Yuan, J.S. (2022). AMPDeep: Hemolytic Activity Prediction of Antimicrobial Peptides using Transfer Learning. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04952-z"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., and Steinegger, M. (2020). ProtTrans: Towards cracking the language of Life\u2019s code through self-supervised deep learning and high performance computing. arXiv.","DOI":"10.1101\/2020.07.12.199554"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Witten, J., and Witten, Z. (2019). Deep learning regression model for antimicrobial peptide design. bioRxiv, 692681.","DOI":"10.1101\/692681"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1021\/ja00393a035","article-title":"Design, synthesis, and characterization of a cytotoxic peptide with melittin-like activity","volume":"103","author":"DeGrado","year":"1981","journal-title":"J. Am. Chem. Soc."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"M\u00f3l, A., Castro, M., and Fontes, W. (2018). NetWheels: A web application to create high quality peptide helical wheel and net projections. bioRxiv, 416347.","DOI":"10.1101\/416347"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Karami, Y., Khakzad, H., Shirazi, H., and Arab, S. (2012, January 2\u20133). Protein structure prediction using bio-inspired algorithm: A review. Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), Shiraz, Fars, Iran.","DOI":"10.1109\/AISP.2012.6313744"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1016\/j.cep.2008.02.009","article-title":"Computer-aided aroma design. II. Quantitative structure\u2013odour relationship","volume":"47","author":"Korichi","year":"2008","journal-title":"Chem. Eng. Process. Process Intensif."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Maccari, G., Di Luca, M., Nifos\u00ed, R., Cardarelli, F., Signore, G., Boccardi, C., and Bifone, A. (2013). Antimicrobial Peptides Design by Evolutionary Multiobjective Optimization. PLoS Comput. Biol., 9.","DOI":"10.1371\/journal.pcbi.1003212"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/S0014-5793(01)02648-5","article-title":"Optimization of the antimicrobial activity of magainin peptides by modification of charge","volume":"501","author":"Dathe","year":"2001","journal-title":"FEBS Lett."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.chempr.2018.01.005","article-title":"Using Evolutionary Algorithms and Machine Learning to Explore Sequence Space for the Discovery of Antimicrobial Peptides","volume":"4","author":"Yoshida","year":"2018","journal-title":"Chem"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Boone, K., Wisdom, C., Camarda, K., Spencer, P., and Tamerler, C. (2021). Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides. BMC Bioinform., 22.","DOI":"10.1186\/s12859-021-04156-x"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1080\/019697298125470","article-title":"Rough Set Theory and Its Applications to Data Analysis","volume":"29","author":"Pawlak","year":"1998","journal-title":"Cybern. Syst."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"12688","DOI":"10.1021\/bi00165a020","article-title":"Design of model amphipathic peptides having potent antimicrobial activities","volume":"31","author":"Blondelle","year":"1992","journal-title":"Biochemistry"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Chowdhary, K.R. (2020). Natural Language Processing. Fundamentals of Artificial Intelligence, Springer.","DOI":"10.1007\/978-81-322-3972-7"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Singh, S.P., Kumar, A., Darbari, H., Singh, L., Rastogi, A., and Jain, S. (2017, January 1\u20132). Machine translation using deep learning: An overview. Proceedings of the 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jaipur, India.","DOI":"10.1109\/COMPTELIX.2017.8003957"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1038\/s41467-020-18073-9","article-title":"Transforming machine translation: A deep learning system reaches news translation quality comparable to human professionals","volume":"11","author":"Popel","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Mutabazi, E., Ni, J., Tang, G., and Cao, W. (2021). A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches. Appl. Sci., 11.","DOI":"10.3390\/app11125456"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.procs.2018.05.090","article-title":"Deep Learning Approaches for Question Answering System","volume":"132","author":"Sharma","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, X., Zhang, S., Wang, H., and Zhang, W. (2019). Neural Machine Reading Comprehension: Methods and Trends. Appl. Sci., 9.","DOI":"10.3390\/app9183698"},{"key":"ref_178","unstructured":"Yin, W., Kann, K., Yu, M., and Sch\u00fctze, H. (2017). Comparative Study of CNN and RNN for Natural Language Processing. arXiv."},{"key":"ref_179","first-page":"377","article-title":"Efficient processing of GRU based on word embedding for text classification","volume":"3","author":"Zulqarnain","year":"2019","journal-title":"JOIV Int. J. Inform. Vis."},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv.","DOI":"10.18653\/v1\/P19-1355"},{"key":"ref_181","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training, OpenAI."},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Kamath, U., Graham, K.L., and Emara, W. (2022). Bidirectional encoder representations from transformers (BERT). Transformers for Machine Learning, Chapman and Hall\/CRC.","DOI":"10.1201\/9781003170082"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1038\/nature05233","article-title":"A linguistic model for the rational design of antimicrobial peptides","volume":"443","author":"Loose","year":"2006","journal-title":"Nature"},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Bolatchiev, A., Baturin, V., Shchetinin, E., and Bolatchieva, E. (2022). Novel Antimicrobial Peptides Designed Using a Recurrent Neural Network Reduce Mortality in Experimental Sepsis. Antibiotics, 11.","DOI":"10.3390\/antibiotics11030411"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"D1094","DOI":"10.1093\/nar\/gkv1051","article-title":"CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides","volume":"44","author":"Waghu","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_187","unstructured":"Das, P., Wadhawan, K., Chang, O., Sercu, T., Santos, C.D., Riemer, M., Chenthamarakshan, V., Padhi, I., and Mojsilovic, A. (2018). PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences. arXiv."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules","volume":"4","author":"Wei","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"1700123","DOI":"10.1002\/minf.201700123","article-title":"Application of generative autoencoder in de novo molecular design","volume":"37","author":"Blaschke","year":"2018","journal-title":"Mol. Inform."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"20746","DOI":"10.1021\/acsomega.0c00442","article-title":"Variational Autoencoder for Generation of Antimicrobial Peptides","volume":"5","author":"Dean","year":"2020","journal-title":"ACS Omega"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/s41551-021-00689-x","article-title":"Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations","volume":"5","author":"Das","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_193","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems 27 (NIPS 2014), Curran Associates, Inc."},{"key":"ref_194","unstructured":"Kushwaha, V., and Nandi, G.C. (2020, January 3\u20135). Study of Prevention of Mode Collapse in Generative Adversarial Network (GAN). Proceedings of the 2020 IEEE 4th Conference on Information & Communication Technology (CICT), Chennai, India."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"22847","DOI":"10.1021\/acsomega.0c02088","article-title":"Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks","volume":"5","author":"Tucs","year":"2020","journal-title":"ACS Omega"},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Liu, S., Lin, Y., Liu, J., Chen, X., Ma, C., Xi, X., Zhou, M., Chen, T., Burrows, J.F., and Wang, L. (2022). Targeted Modification and Structure-Activity Study of GL-29, an Analogue of the Antimicrobial Peptide Palustrin-2ISb. Antibiotics, 11.","DOI":"10.3390\/antibiotics11081048"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"698477","DOI":"10.3389\/fgene.2021.698477","article-title":"ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation","volume":"12","author":"Chen","year":"2021","journal-title":"Front. Genet."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Lee, B., Shin, M.K., Hwang, I.W., Jung, J., Shim, Y.J., Kim, G.W., Kim, S.T., Jang, W., and Sung, J.S. (2021). A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222212291"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"106533","DOI":"10.1016\/j.compchemeng.2019.106533","article-title":"ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework","volume":"131","author":"Han","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"baac011","DOI":"10.1093\/database\/baac011","article-title":"A review on antimicrobial peptides databases and the computational tools","volume":"2022","author":"Ramazi","year":"2022","journal-title":"Database"},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Xiao, X., and You, Z.B. (2015, January 14\u201316). Predicting minimum inhibitory concentration of antimicrobial peptides by the pseudo-amino acid composition and Gaussian kernel regression. Proceedings of the 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, China.","DOI":"10.1109\/BMEI.2015.7401519"},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jtbi.2017.02.012","article-title":"Antimicrobial potency of cationic antimicrobial peptides can be predicted from their amino acid composition: Application to the detection of \u201ccryptic\u201d antimicrobial peptides","volume":"419","author":"Pane","year":"2017","journal-title":"J. Theor. Biol."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"4932","DOI":"10.1039\/D0CS01026J","article-title":"Chemically modified and conjugated antimicrobial peptides against superbugs","volume":"50","author":"Li","year":"2021","journal-title":"Chem. Soc. Rev."},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Di Natale, C., De Benedictis, I., De Benedictis, A., and Marasco, D. (2020). Metal\u2013peptide complexes as promising antibiotics to fight emerging drug resistance: New perspectives in tuberculosis. Antibiotics, 9.","DOI":"10.3390\/antibiotics9060337"},{"key":"ref_206","doi-asserted-by":"crossref","unstructured":"La Manna, S., Di Natale, C., Onesto, V., and Marasco, D. (2021). Self-Assembling Peptides: From Design to Biomedical Applications. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222312662"},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Chen, C.H., and Lu, T.K. (2020). Development and Challenges of Antimicrobial Peptides for Therapeutic Applications. Antibiotics, 9.","DOI":"10.3390\/antibiotics9010024"},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Mathur, D., Singh, S., Mehta, A., Agrawal, P., and Raghava, G.P.S. (2018). In silico approaches for predicting the half-life of natural and modified peptides in blood. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196829"},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Sharma, A., Singla, D., Rashid, M., and Raghava, G.P.S. (2014). Designing of peptides with desired half-life in intestine-like environment. BMC Bioinform., 15.","DOI":"10.1186\/1471-2105-15-282"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"42717","DOI":"10.1038\/srep42717","article-title":"SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules","volume":"7","author":"Daina","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"W257","DOI":"10.1093\/nar\/gky318","article-title":"ProTox-II: A webserver for the prediction of toxicity of chemicals","volume":"46","author":"Banerjee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Consortium, O.S.D.D., and Raghava, G.P.S. (2013). In Silico Approach for Predicting Toxicity of Peptides and Proteins. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073957"},{"key":"ref_213","unstructured":"Taho, F. (2020). Antimicrobial Peptide Host Toxicity Prediction with Transfer Learning for Proteins. [Ph.D. Thesis, University of British Columbia]."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Hicks, A.L., Wheeler, N., S\u00e1nchez-Bus\u00f3, L., Rakeman, J.L., Harris, S.R., and Grad, Y.H. (2019). Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data. PLoS Comput. Biol., 15.","DOI":"10.1101\/607127"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"100073","DOI":"10.1016\/j.patter.2020.100073","article-title":"dtoolai: Reproducibility for deep learning","volume":"1","author":"Hartley","year":"2020","journal-title":"Patterns"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"211860","DOI":"10.1109\/ACCESS.2020.3039833","article-title":"Challenges for the repeatability of deep learning models","volume":"8","author":"Alahmari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Pham, H.V., Qian, S., Wang, J., Lutellier, T., Rosenthal, J., Tan, L., Yu, Y., and Nagappan, N. (2020, January 21\u201325). Problems and opportunities in training deep learning software systems: An analysis of variance. Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering, Melbourne, Australia.","DOI":"10.1145\/3324884.3416545"},{"key":"ref_218","unstructured":"Gundersen, O.E., Coakley, K., and Kirkpatrick, C. (2022). Sources of Irreproducibility in Machine Learning: A Review. arXiv."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."}],"container-title":["Antibiotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6382\/11\/10\/1451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:59:02Z","timestamp":1760144342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6382\/11\/10\/1451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":220,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["antibiotics11101451"],"URL":"https:\/\/doi.org\/10.3390\/antibiotics11101451","relation":{},"ISSN":["2079-6382"],"issn-type":[{"value":"2079-6382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}