{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T20:51:27Z","timestamp":1778187087376,"version":"3.51.4"},"reference-count":106,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100003141","name":"CONACYT","doi-asserted-by":"publisher","award":["A1-S-20638"],"award-info":[{"award-number":["A1-S-20638"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada","award":["501\/2018"],"award-info":[{"award-number":["501\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to become a plausible option to fight multi-drug resistant bacteria as well as other pathogens. Quantitative sequence-activity models (QSAMs) have been helpful to discover new AMPs because they allow to explore a large universe of peptide sequences and help reduce the number of wet lab experiments. A main aspect in the building of QSAMs based on shallow learning is to determine an optimal set of protein descriptors (features) required to discriminate between sequences with different antimicrobial activities. These features are generally handcrafted from peptide sequence datasets that are labeled with specific antimicrobial activities. However, recent developments have shown that unsupervised approaches can be used to determine features that outperform human-engineered (handcrafted) features. Thus, knowing which of these two approaches contribute to a better classification of AMPs, it is a fundamental question in order to design more accurate models. Here, we present a systematic and rigorous study to compare both types of features. Experimental outcomes show that non-handcrafted features lead to achieve better performances than handcrafted features. However, the experiments also prove that an improvement in performance is achieved when both types of features are merged. A relevance analysis reveals that non-handcrafted features have higher information content than handcrafted features, while an interaction-based importance analysis reveals that handcrafted features are more important. These findings suggest that there is complementarity between both types of features. Comparisons regarding state-of-the-art deep models show that shallow models yield better performances both when fed with non-handcrafted features alone and when fed with non-handcrafted and handcrafted features together.<\/jats:p>","DOI":"10.1093\/bib\/bbac428","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T20:28:56Z","timestamp":1665433736000},"source":"Crossref","is-referenced-by-count":22,"title":["Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3962-7658","authenticated-orcid":false,"given":"C\u00e9sar R","family":"Garc\u00eda-Jacas","sequence":"first","affiliation":[{"name":"C\u00e1tedras CONACYT - Departamento de Ciencias de la Computaci\u00f3n, Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada (CICESE) , 22860 Ensenada, Baja California, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-3345","authenticated-orcid":false,"given":"Luis A","family":"Garc\u00eda-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n, Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada (CICESE) , 22860 Ensenada, Baja California, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Martinez-Rios","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana , Ciudad de M\u00e9xico, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Issac P","family":"Tapia-Contreras","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n, Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada (CICESE) , 22860 Ensenada, Baja California, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4621-0380","authenticated-orcid":false,"given":"Carlos A","family":"Brizuela","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n, Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada (CICESE) , 22860 Ensenada, Baja California, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"2022112111120465200_ref1","volume-title":"Antimicrobial resistance","author":"WHO"},{"key":"2022112111120465200_ref2","volume-title":"Antibiotic\/Antimicrobial Resistance (AR\/AMR)","author":"CDC"},{"key":"2022112111120465200_ref3","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/S1473-3099(18)30605-4","article-title":"Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European economic area in 2015: a population-level modelling analysis","volume":"19","author":"Cassini","year":"2019","journal-title":"Lancet Infect Dis"},{"key":"2022112111120465200_ref4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/S1473-3099(18)30648-0","article-title":"Public health burden of antimicrobial resistance in Europe","volume":"19","author":"Tacconelli","year":"2019","journal-title":"Lancet Infect Dis"},{"key":"2022112111120465200_ref5","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S1473-3099(18)30708-4","article-title":"Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in Switzerland","volume":"19","author":"Gasser","year":"2019","journal-title":"Lancet Infect Dis"},{"key":"2022112111120465200_ref6","doi-asserted-by":"crossref","first-page":"3903","DOI":"10.2147\/IDR.S234610","article-title":"Antimicrobial resistance: implications and costs","volume":"12","author":"Dadgostar","year":"2019","journal-title":"Infect Drug Resist"},{"key":"2022112111120465200_ref7","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/S1473-3099(13)70318-9","article-title":"Antibiotic resistance\u2014the need for global solutions","volume":"13","author":"Laxminarayan","year":"2013","journal-title":"Lancet Infect Dis"},{"key":"2022112111120465200_ref8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2022112111120465200_ref9","volume-title":"Centers for Disease Control and Prevention","author":"CDC","year":"2019"},{"key":"2022112111120465200_ref10","doi-asserted-by":"crossref","first-page":"R14","DOI":"10.1016\/j.cub.2015.11.017","article-title":"Antimicrobial peptides","volume":"26","author":"Zhang","year":"2016","journal-title":"Curr Biol"},{"key":"2022112111120465200_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fmicb.2018.00325","article-title":"Designing antibacterial peptides with enhanced killing kinetics","volume":"9","author":"Waghu","year":"2018","journal-title":"Front Microbiol"},{"key":"2022112111120465200_ref12","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1039\/C8NP00031J","article-title":"Nonribosomal antibacterial peptides that target multidrug-resistant bacteria","volume":"36","author":"Liu","year":"2019","journal-title":"Nat Prod Rep"},{"key":"2022112111120465200_ref13","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.1111\/j.1742-4658.2009.07358.x","article-title":"Multifunctional host defense peptides: antiparasitic activities","volume":"276","author":"Mor","year":"2009","journal-title":"FEBS J"},{"key":"2022112111120465200_ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fmicb.2016.00091","article-title":"Anti-parasitic peptides from arthropods and their application in drug therapy","volume":"7","author":"Lacerda","year":"2016","journal-title":"Front Microbiol"},{"key":"2022112111120465200_ref15","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/10_2013_191","volume-title":"Yellow Biotechnology I: Insect Biotechnologie in Drug Discovery and Preclinical Research","author":"Pretzel","year":"2013"},{"key":"2022112111120465200_ref16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.peptides.2019.02.006","article-title":"Antiaflatoxigenic effects of selected antifungal peptides","volume":"115","author":"Devi","year":"2019","journal-title":"Peptides"},{"key":"2022112111120465200_ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fcimb.2020.00105","article-title":"Antifungal peptides as therapeutic agents","volume":"10","author":"Fern\u00e1ndez de Ullivarri","year":"2020","journal-title":"Front Cell Infect Microbiol"},{"key":"2022112111120465200_ref18","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1007\/s00018-019-03138-w","article-title":"Antiviral peptides as promising therapeutic drugs","volume":"76","author":"Vilas Boas","year":"2019","journal-title":"Cell Mol Life Sci"},{"key":"2022112111120465200_ref19","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.2174\/0929867326666190805151654","article-title":"Antiviral activities of human host defense peptides","volume":"27","author":"David","year":"2020","journal-title":"Curr Med Chem"},{"key":"2022112111120465200_ref20","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/S2213-8587(19)30249-9","article-title":"Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials","volume":"7","author":"Kristensen","year":"2019","journal-title":"Lancet Diabetes Endocrinol"},{"key":"2022112111120465200_ref21","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.semcdb.2018.04.006","article-title":"Human antimicrobial peptides and cancer","volume":"88","author":"Jin","year":"2019","journal-title":"Semin Cell Dev Biol"},{"key":"2022112111120465200_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fonc.2019.00341","article-title":"Human Beta Defensins and cancer: contradictions and common ground","volume":"9","author":"Ghosh","year":"2019","journal-title":"Front Oncol"},{"key":"2022112111120465200_ref23","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1016\/j.bmc.2017.06.052","article-title":"Therapeutic peptides: historical perspectives, current development trends, and future directions","volume":"26","author":"Lau","year":"2018","journal-title":"Bioorg Med Chem"},{"key":"2022112111120465200_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fmicb.2020.582779","article-title":"Antimicrobial peptides: classification, design, application and research Progress in multiple fields","volume":"11","author":"Huan","year":"2020","journal-title":"Front Microbiol"},{"key":"2022112111120465200_ref25","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/978-1-4939-2285-7_9","volume-title":"Computational Peptidology","author":"Maccari","year":"2015"},{"key":"2022112111120465200_ref26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-1-4939-2285-7_2","volume-title":"Computational Peptidology","author":"Kuczera","year":"2015"},{"key":"2022112111120465200_ref27","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-1-4939-2285-7_7","volume-title":"Computational Peptidology","author":"Gupta","year":"2015"},{"key":"2022112111120465200_ref28","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1093\/bioinformatics\/btr604","article-title":"AMPA: an automated web server for prediction of protein antimicrobial regions","volume":"28","author":"Torrent","year":"2011","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref29","doi-asserted-by":"crossref","first-page":"W199","DOI":"10.1093\/nar\/gks450","article-title":"AVPpred: collection and prediction of highly effective antiviral peptides","volume":"40","author":"Thakur","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref30","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1002\/bip.22066","article-title":"Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application","volume":"98","author":"Fernandes","year":"2012","journal-title":"Pept Sci"},{"key":"2022112111120465200_ref31","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TCBB.2012.89","article-title":"ClassAMP: a prediction tool for classification of antimicrobial peptides","volume":"9","author":"Joseph","year":"2012","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022112111120465200_ref32","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":"2022112111120465200_ref33","first-page":"475062","article-title":"A large-scale structural classification of antimicrobial peptides","volume":"2015","author":"Lee","year":"2015","journal-title":"Biomed Res Int"},{"key":"2022112111120465200_ref34","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":"2015","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref35","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.1093\/bioinformatics\/btw560","article-title":"Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types","volume":"32","author":"Lin","year":"2016","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref36","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":"2022112111120465200_ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fmicb.2018.00323","article-title":"In silico approach for prediction of antifungal peptides","volume":"9","author":"Agrawal","year":"2018","journal-title":"Front Microbiol"},{"key":"2022112111120465200_ref38","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":"2022112111120465200_ref39","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":"2022112111120465200_ref40","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":"2018","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref41","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":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022112111120465200_ref42","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1093\/bib\/bbz043","article-title":"Characterization and identification of antimicrobial peptides with different functional activities","volume":"21","author":"Chung","year":"2019","journal-title":"Brief Bioinform"},{"key":"2022112111120465200_ref43","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1186\/s12859-019-2766-9","article-title":"An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies","volume":"20","author":"Lin","year":"2019","journal-title":"BMC Bioinf"},{"key":"2022112111120465200_ref44","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1093\/bioinformatics\/btz246","article-title":"PEPred-suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning","volume":"35","author":"Wei","year":"2019","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref45","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1186\/s12859-019-3327-y","article-title":"Antimicrobial peptide identification using multi-scale convolutional network","volume":"20","author":"Su","year":"2019","journal-title":"BMC Bioinf"},{"key":"2022112111120465200_ref46","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":"2022112111120465200_ref47","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":"2022112111120465200_ref48","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1186\/s12864-020-06978-0","article-title":"ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding","volume":"21","author":"Fu","year":"2020","journal-title":"BMC Genomics"},{"key":"2022112111120465200_ref49","doi-asserted-by":"crossref","first-page":"1","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":"Brief Bioinform"},{"key":"2022112111120465200_ref50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab200","article-title":"A novel antibacterial peptide recognition algorithm based on BERT","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022112111120465200_ref51","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1021\/acs.jcim.1c00251","article-title":"Alignment-free antimicrobial peptide predictors: improving performance by a thorough analysis of the largest available data set","volume":"61","author":"Pinacho-Castellanos","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2022112111120465200_ref52","doi-asserted-by":"crossref","first-page":"1","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":"Brief Bioinform"},{"key":"2022112111120465200_ref53","doi-asserted-by":"crossref","first-page":"8700","DOI":"10.1073\/pnas.92.19.8700","article-title":"Prediction of protein folding class using global description of amino acid sequence","volume":"92","author":"Dubchak","year":"1995","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2022112111120465200_ref54","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, Bioinf"},{"key":"2022112111120465200_ref55","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1093\/bioinformatics\/bth466","article-title":"Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes","volume":"21","author":"Chou","year":"2004","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref56","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1073\/pnas.0607879104","article-title":"Predicting protein\u2013protein interactions based only on sequences information","volume":"104","author":"Shen","year":"2007","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2022112111120465200_ref57","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.phpro.2010.10.013","article-title":"New set of 2D\/3D thermodynamic indices for proteins. A formalism based on \u201cmolten globule\u201d theory","volume":"8","author":"Ruiz-Blanco Yasser","year":"2010","journal-title":"Physics Procedia"},{"key":"2022112111120465200_ref58","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1093\/bioinformatics\/btt196","article-title":"Incorporating key position and amino acid residue features to identify general and species-specific ubiquitin conjugation sites","volume":"29","author":"Chen","year":"2013","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref59","doi-asserted-by":"crossref","first-page":"110039","DOI":"10.1016\/j.jtbi.2019.110039","article-title":"LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs","volume":"485","author":"Marrero-Ponce","year":"2020","journal-title":"J Theor Biol"},{"key":"2022112111120465200_ref60","doi-asserted-by":"crossref","first-page":"W32","DOI":"10.1093\/nar\/gkl305","article-title":"PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence","volume":"34","author":"Li","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref61","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: a python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref62","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1002\/pro.3673","article-title":"ProtDCal-suite: a web server for the numerical codification and functional analysis of proteins","volume":"28","author":"Romero-Molina","year":"2019","journal-title":"Protein Sci"},{"key":"2022112111120465200_ref63","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1021\/acs.jcim.9b00629","article-title":"MuLiMs-MCoMPAs: a novel multiplatform framework to compute tensor algebra-based three-dimensional protein descriptors","volume":"60","author":"Contreras-Torres","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2022112111120465200_ref64","doi-asserted-by":"crossref","first-page":"18074","DOI":"10.1038\/s41598-020-75029-1","article-title":"Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach","volume":"10","author":"Aguilera-Mendoza","year":"2020","journal-title":"Sci Rep"},{"key":"2022112111120465200_ref65","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1002\/prot.26003","article-title":"PeptiDesCalculator: software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints","volume":"89","author":"Barigye","year":"2021","journal-title":"Proteins: Struct, Funct, Bioinf"},{"key":"2022112111120465200_ref66","doi-asserted-by":"crossref","DOI":"10.1002\/9783527628766","volume-title":"Molecular Descriptors for Chemoinformatics","author":"Todeschini","year":"2009"},{"key":"2022112111120465200_ref67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.11.008","article-title":"Ensembles for feature selection: a review and future trends","volume":"52","author":"Bol\u00f3n-Canedo","year":"2019","journal-title":"Inf Fusion"},{"key":"2022112111120465200_ref68","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.1007\/s00521-019-04082-3","article-title":"Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains","volume":"32","author":"Pes","year":"2020","journal-title":"Neural Comput Applic"},{"key":"2022112111120465200_ref69","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1126\/science.146.3651.1593","article-title":"Protein structure relationships revealed by mutational analysis","volume":"146","author":"Yanofsky","year":"1964","journal-title":"Science"},{"key":"2022112111120465200_ref70","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/0022-2836(87)90352-4","article-title":"Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus","volume":"193","author":"Altschuh","year":"1987","journal-title":"J Mol Biol"},{"key":"2022112111120465200_ref71","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1093\/protein\/2.3.193","article-title":"Coordinated amino acid changes in homologous protein families*","volume":"2","author":"Altschuh","year":"1988","journal-title":"Protein Eng Des Sel"},{"key":"2022112111120465200_ref72","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/PL00006191","article-title":"Coordinated amino acid changes in the evolution of mammalian Defensins","volume":"44","author":"Hughes","year":"1997","journal-title":"J Mol Evol"},{"key":"2022112111120465200_ref73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/biomethods\/bpac008","article-title":"PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles","volume":"7","author":"Mohammadi","year":"2022","journal-title":"Biol Methods Protoc"},{"key":"2022112111120465200_ref74","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13059-017-1319-7","article-title":"Alignment-free sequence comparison: benefits, applications, and tools","volume":"18","author":"Zielezinski","year":"2017","journal-title":"Genome Biol"},{"key":"2022112111120465200_ref75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbac094","article-title":"Do deep learning models make a difference in the identification of antimicrobial peptides?","volume":"23","author":"Garc\u00eda-Jacas","year":"2022","journal-title":"Brief Bioinform"},{"key":"2022112111120465200_ref76","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1093\/bioinformatics\/btv180","article-title":"Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences","volume":"31","author":"Aguilera-Mendoza","year":"2015","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref77","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1093\/bioinformatics\/btz260","article-title":"Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis","volume":"35","author":"Aguilera-Mendoza","year":"2019","journal-title":"Bioinformatics"},{"key":"2022112111120465200_ref78","first-page":"1","volume-title":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","author":"Oyedare","year":"2019"},{"key":"2022112111120465200_ref79","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1021\/acs.jcim.9b01184","article-title":"Boosting tree-assisted multitask deep learning for small scientific datasets","volume":"60","author":"Jiang","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2022112111120465200_ref80","first-page":"1","article-title":"Deep learning for road traffic forecasting: does it make a difference?","volume":"23","author":"Manibardo","year":"2021","journal-title":"IEEE trans Intell Transp Syst"},{"key":"2022112111120465200_ref81","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"Consortium TU","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref82","doi-asserted-by":"crossref","first-page":"D412","DOI":"10.1093\/nar\/gkaa913","article-title":"Pfam: the protein families database in 2021","volume":"49","author":"Mistry","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref83","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 U S A"},{"key":"2022112111120465200_ref84","doi-asserted-by":"crossref","first-page":"D154","DOI":"10.1093\/nar\/gki070","article-title":"The universal protein resource (UniProt)","volume":"33","author":"Bairoch","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2022112111120465200_ref85","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1093\/bib\/bbz041","article-title":"iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA","volume":"21","author":"Chen","year":"2019","journal-title":"RNA and protein sequence data, Briefings Bioinf"},{"key":"2022112111120465200_ref86","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1021\/ci000321u","article-title":"Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations","volume":"40","author":"Godden","year":"2000","journal-title":"J Chem Inf Comput Sci"},{"key":"2022112111120465200_ref87","volume-title":"Correlation-based Feature Selection for Machine Learning. Department of Computer Science","author":"Hall","year":"1999"},{"key":"2022112111120465200_ref88","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of ReliefF and RReliefF","volume":"53","author":"Robnik-\u0160ikonja","year":"2003","journal-title":"Mach Learn"},{"key":"2022112111120465200_ref89","author":"WEKA software"},{"key":"2022112111120465200_ref90","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s11030-014-9565-z","article-title":"IMMAN: free software for information theory-based chemometric analysis","volume":"19","author":"Urias","year":"2015","journal-title":"Mol Divers"},{"key":"2022112111120465200_ref91","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif Intell"},{"key":"2022112111120465200_ref92","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2022112111120465200_ref93","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1023\/A:1025386326946","article-title":"Rational selection of training and test sets for the development of validated QSAR models","volume":"17","author":"Golbraikh","year":"2003","journal-title":"J Comput Aided Mol Des"},{"key":"2022112111120465200_ref94","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/978-3-030-66515-9_13","volume-title":"Black Box Optimization, Machine Learning, and No-Free Lunch Theorems","author":"Wolpert","year":"2021"},{"key":"2022112111120465200_ref95","first-page":"1","article-title":"Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis","volume":"18","author":"Benavoli","year":"2017","journal-title":"J Mach Learn Res"},{"key":"2022112111120465200_ref96","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1002\/wics.75","article-title":"Ockham's razor","volume":"2","author":"Lazar","year":"2010","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"2022112111120465200_ref97","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2022112111120465200_ref98","doi-asserted-by":"crossref","first-page":"114069","DOI":"10.1016\/j.ab.2020.114069","article-title":"Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations","volume":"615","author":"Naseer","year":"2021","journal-title":"Anal Biochem"},{"key":"2022112111120465200_ref99","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-1-4419-9326-7_11","volume-title":"Ensemble Machine Learning: Methods and Applications","author":"Qi","year":"2012"},{"key":"2022112111120465200_ref100","first-page":"1","article-title":"All models are wrong, but many are useful: learning a Variable's importance by studying an entire class of prediction models simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J Mach Learn Res"},{"key":"2022112111120465200_ref101","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1214\/07-AOAS148","article-title":"Predictive learning via rule ensembles","volume":"2","author":"Friedman","year":"2008","journal-title":"Ann Appl Stat"},{"key":"2022112111120465200_ref102","volume-title":"iml: Interpretable Machine Learning","author":"Molnar"},{"key":"2022112111120465200_ref103","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF01200821","article-title":"Generalized molecular descriptors","volume":"7","author":"Randi\u0107","year":"1991","journal-title":"J Math Chem"},{"key":"2022112111120465200_ref104","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":"2022112111120465200_ref105","doi-asserted-by":"crossref","first-page":"1","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":"Brief Bioinform"},{"key":"2022112111120465200_ref106","first-page":"1","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":"2021","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac428\/47143868\/bbac428.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac428\/47143868\/bbac428.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T04:45:30Z","timestamp":1727930730000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac428\/6754757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":106,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac428","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,11]]},"published":{"date-parts":[[2022,10,10]]},"article-number":"bbac428"}}