{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:30:54Z","timestamp":1769585454185,"version":"3.49.0"},"reference-count":99,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first generation of these predictors was filled with what is now known as shallow learning-based models. These models require the computation and selection of molecular descriptors to characterize each peptide sequence and train the models. The second generation, known as deep learning-based models, which no longer requires the explicit computation and selection of those descriptors, started to be used in the prediction task of AMPs just four years ago. The superior performance claimed by deep models regarding shallow models has created a prevalent inertia to using deep learning to identify AMPs. However, methodological flaws and\/or modeling biases in the building of deep models do not support such superiority. Here, we analyze the main pitfalls that led to establish biased conclusions on the leading performance of deep models. Also, we analyze whether deep models truly contribute to achieve better predictions than shallow models by performing fair studies on different state-of-the-art benchmarking datasets. The experiments reveal that deep models do not outperform shallow models in the classification of AMPs, and that both types of models codify similar chemical information since their predictions are highly similar. Thus, according to the currently available datasets, we conclude that the use of deep learning could not be the most suitable approach to develop models to identify AMPs, mainly because shallow models achieve comparable-to-superior performances and are simpler (Ockham\u2019s razor principle). Even so, we suggest the use of deep learning only when its capabilities lead to obtaining significantly better performance gains worth the additional computational cost.<\/jats:p>","DOI":"10.1093\/bib\/bbac094","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T12:09:10Z","timestamp":1645704550000},"source":"Crossref","is-referenced-by-count":42,"title":["Do deep learning models make a difference in the identification of antimicrobial peptides?"],"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 \u2013 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5783-352X","authenticated-orcid":false,"given":"Sergio A","family":"Pinacho-Castellanos","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"},{"name":"Centro de Investigaci\u00f3n y Desarrollo de Tecnolog\u00eda Digital (CITEDI), Instituto Polit\u00e9cnico Nacional (IPN), 22435 Tijuana, Baja California, M\u00e9xico"}]},{"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"}]},{"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"}]}],"member":"286","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"2022051813454565700_ref1","doi-asserted-by":"crossref","first-page":"S19","DOI":"10.1186\/1471-2105-11-S1-S19","article-title":"AntiBP2: improved version of antibacterial peptide prediction","volume":"11","author":"Lata","year":"2010","journal-title":"BMC Bioinf"},{"key":"2022051813454565700_ref2","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":"2022051813454565700_ref3","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":"2022051813454565700_ref4","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":"2022051813454565700_ref5","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":"2022051813454565700_ref6","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":"2022051813454565700_ref7","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":"2022051813454565700_ref8","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":"2022051813454565700_ref9","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":"2022051813454565700_ref10","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":"2022051813454565700_ref11","doi-asserted-by":"crossref","first-page":"323","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":"2022051813454565700_ref12","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":"2022051813454565700_ref13","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":"2022051813454565700_ref14","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":"2022051813454565700_ref15","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":"2022051813454565700_ref16","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":"2022051813454565700_ref17","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":"2022051813454565700_ref18","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":"2022051813454565700_ref19","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":"2022051813454565700_ref20","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":"2022051813454565700_ref21","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":"2022051813454565700_ref22","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":"2022051813454565700_ref23","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":"Brief Bioinform"},{"key":"2022051813454565700_ref24","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":"Brief Bioinform"},{"key":"2022051813454565700_ref25","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":"2022051813454565700_ref26","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":"Brief Bioinform"},{"key":"2022051813454565700_ref27","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":"Brief Bioinform"},{"key":"2022051813454565700_ref28","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":"2021","journal-title":"Brief Bioinform"},{"key":"2022051813454565700_ref29","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2021.3130825","article-title":"Deep-AVPpred: artificial intelligence driven discovery of peptide drugs for viral infections","author":"Sharma","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"2022051813454565700_ref30","doi-asserted-by":"crossref","first-page":"704","DOI":"10.3390\/v11080704","article-title":"Human antimicrobial peptides as therapeutics for viral infections","volume":"11","author":"Ahmed","year":"2019","journal-title":"Viruses"},{"key":"2022051813454565700_ref31","doi-asserted-by":"crossref","first-page":"e0181748","DOI":"10.1371\/journal.pone.0181748","article-title":"THPdb: database of FDA-approved peptide and protein therapeutics","volume":"12","author":"Usmani","year":"2017","journal-title":"PLoS One"},{"key":"2022051813454565700_ref32","volume-title":"Antimicrobial resistance","author":"WHO"},{"key":"2022051813454565700_ref33","volume-title":"Antibiotic\/Antimicrobial Resistance (AR\/AMR)","author":"CDC"},{"key":"2022051813454565700_ref34","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":"2022051813454565700_ref35","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":"2022051813454565700_ref36","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":"2022051813454565700_ref37","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":"2022051813454565700_ref38","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":"2022051813454565700_ref39","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":"2022051813454565700_ref40","doi-asserted-by":"crossref","first-page":"325","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":"2022051813454565700_ref41","doi-asserted-by":"crossref","first-page":"91","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":"2022051813454565700_ref42","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":"2022051813454565700_ref43","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":"2022051813454565700_ref44","doi-asserted-by":"crossref","first-page":"105","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":"2022051813454565700_ref45","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":"2022051813454565700_ref46","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":"2022051813454565700_ref47","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":"2022051813454565700_ref48","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":"2022051813454565700_ref49","doi-asserted-by":"crossref","first-page":"341","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":"2022051813454565700_ref50","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":"2022051813454565700_ref51","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":"2022051813454565700_ref52","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":"2022051813454565700_ref53","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":"2022051813454565700_ref54","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":"2022051813454565700_ref55","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin"},{"key":"2022051813454565700_ref56","first-page":"50","volume-title":"On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks","author":"Soekhoe","year":"2016"},{"key":"2022051813454565700_ref57","first-page":"1","volume-title":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","author":"Oyedare","year":"2019"},{"key":"2022051813454565700_ref58","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":"2022051813454565700_ref59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2021.3083957","article-title":"Deep learning for road traffic forecasting: does it make a difference?","author":"Manibardo","year":"2021","journal-title":"IEEE trans Intell Transp Syst"},{"key":"2022051813454565700_ref60","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1002\/qsar.200390007","article-title":"The importance of being Earnest: validation is the absolute essential for successful application and interpretation of QSPR models","volume":"22","author":"Tropsha","year":"2003","journal-title":"QSAR Comb Sci"},{"key":"2022051813454565700_ref61","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol Inf"},{"key":"2022051813454565700_ref62","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":"2022051813454565700_ref63","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, RNA and protein sequence data","volume":"21","author":"Chen","year":"2019","journal-title":"Brief Bioinform"},{"key":"2022051813454565700_ref64","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":"2022051813454565700_ref65","first-page":"403","volume-title":"EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing","author":"Niranjan","year":"2019"},{"key":"2022051813454565700_ref66","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.jtbi.2005.05.034","article-title":"Using LogitBoost classifier to predict protein structural classes","volume":"238","author":"Cai","year":"2006","journal-title":"J Theor Biol"},{"key":"2022051813454565700_ref67","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10994-014-5434-3","article-title":"An improved multiclass LogitBoost using adaptive-one-vs-one","volume":"97","author":"Sun","year":"2014","journal-title":"Mach Learn"},{"key":"2022051813454565700_ref68","author":"WEKA software"},{"key":"2022051813454565700_ref69","first-page":"10\/11","volume-title":"A DERA\/IEE Workshop on Intelligent Sensor Processing (Ref. No. 2001\/050)","author":"Kuncheva","year":"2001"},{"key":"2022051813454565700_ref70","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy","volume":"51","author":"Kuncheva","year":"2003","journal-title":"Mach Learn"},{"key":"2022051813454565700_ref71","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.patrec.2004.08.019","article-title":"Using diversity measures for generating error-correcting output codes in classifier ensembles","volume":"26","author":"Kuncheva","year":"2005","journal-title":"Pattern Recognit Lett"},{"key":"2022051813454565700_ref72","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1073\/pnas.89.22.10915","article-title":"Amino acid substitution matrices from protein blocks","volume":"89","author":"Henikoff","year":"1992","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2022051813454565700_ref73","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1039\/D0CS00098A","article-title":"QSAR without borders","volume":"49","author":"Muratov","year":"2020","journal-title":"Chem Soc Rev"},{"key":"2022051813454565700_ref74","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1002\/prot.21770","article-title":"Sequence-similar, structure-dissimilar protein pairs in the PDB","volume":"71","author":"Kosloff","year":"2008","journal-title":"Proteins: Struct, Funct, Bioinf"},{"key":"2022051813454565700_ref75","doi-asserted-by":"crossref","first-page":"1600118","DOI":"10.1002\/minf.201600118","article-title":"Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR","volume":"36","author":"Winkler","year":"2017","journal-title":"Mol Inf"},{"key":"2022051813454565700_ref76","first-page":"243","article-title":"Ockham's razor, Wiley Interdiscip","volume":"2","author":"Lazar","year":"2010","journal-title":"Rev Comput Stat"},{"key":"2022051813454565700_ref77","first-page":"2653","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":"2022051813454565700_ref78","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math Control Signals, Syst"},{"key":"2022051813454565700_ref79","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":"2022051813454565700_ref80","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"Inf Fusion"},{"key":"2022051813454565700_ref81","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.01.008","article-title":"Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI","volume":"71","author":"Holzinger","year":"2021","journal-title":"Inf Fusion"},{"key":"2022051813454565700_ref82","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"key":"2022051813454565700_ref83","doi-asserted-by":"crossref","DOI":"10.1002\/9783527628766","volume-title":"Molecular Descriptors for Chemoinformatics","author":"Todeschini","year":"2009"},{"key":"2022051813454565700_ref84","doi-asserted-by":"crossref","DOI":"10.1002\/9781118914564","volume-title":"Ensemble Feature Selection. Combining Pattern Classifiers: Methods and Algorithms","author":"Kuncheva","year":"2014"},{"key":"2022051813454565700_ref85","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":"2022051813454565700_ref86","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"2022051813454565700_ref87","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1080\/02564602.2014.906859","article-title":"A review of ensemble learning based feature selection","volume":"31","author":"Guan","year":"2014","journal-title":"IETE Tech Rev"},{"key":"2022051813454565700_ref88","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.3390\/app8091521","article-title":"Swarm intelligence algorithms for feature selection: a review","volume":"8","author":"Brezo\u010dnik","year":"2018","journal-title":"Appl Sci"},{"key":"2022051813454565700_ref89","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J Biomed Inform"},{"key":"2022051813454565700_ref90","doi-asserted-by":"crossref","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","article-title":"Metaheuristic algorithms on feature selection: a survey of one decade of research (2009-2019)","volume":"9","author":"Agrawal","year":"2021","journal-title":"IEEE Access"},{"key":"2022051813454565700_ref91","first-page":"1","article-title":"Ensemble learning: a survey, Wiley Interdiscip","volume":"8","author":"Sagi","year":"2018","journal-title":"Rev Data Min Knowl Discov"},{"key":"2022051813454565700_ref92","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","article-title":"Is combining classifiers with stacking better than selecting the best one?","volume":"54","author":"D\u017eeroski","year":"2004","journal-title":"Mach Learn"},{"key":"2022051813454565700_ref93","first-page":"124","volume-title":"9th International Workshop, MCS","author":"Brown","year":"2010"},{"key":"2022051813454565700_ref94","first-page":"3062","volume-title":"AdaBoost algorithm with random forests for predicting breast cancer survivability","author":"Thongkam","year":"2008"},{"key":"2022051813454565700_ref95","doi-asserted-by":"crossref","first-page":"26190","DOI":"10.1109\/ACCESS.2017.2766844","article-title":"A LogitBoost-based algorithm for detecting known and unknown web attacks","volume":"5","author":"Kamarudin","year":"2017","journal-title":"IEEE Access"},{"key":"2022051813454565700_ref96","first-page":"186","article-title":"An enhanced and secured predictive model of Ada-boost and random-Forest techniques in HCV detections","volume":"51","author":"Jadhav","year":"2021","journal-title":"Materials Today: Proceedings"},{"key":"2022051813454565700_ref97","doi-asserted-by":"crossref","first-page":"397","DOI":"10.12688\/f1000research.52676.1","article-title":"Rationality over fashion and hype in drug design [version 1; peer review: 2 approved]","volume":"10","author":"Medina-Franco","year":"2021","journal-title":"F1000Research"},{"key":"2022051813454565700_ref98","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","article-title":"A review on the long short-term memory model","volume":"53","author":"Van Houdt","year":"2020","journal-title":"Artif Intell Rev"},{"key":"2022051813454565700_ref99","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"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/3\/bbac094\/43745073\/bbac094.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/3\/bbac094\/43745073\/bbac094.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T05:09:52Z","timestamp":1726722592000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac094\/6563422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,6]]},"references-count":99,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5,13]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac094","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,5]]},"published":{"date-parts":[[2022,4,6]]},"article-number":"bbac094"}}