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This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20\u201325 residues long than peptides in other length ranges.<\/jats:p>","DOI":"10.1093\/bib\/bby124","type":"journal-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T20:10:15Z","timestamp":1543608615000},"page":"408-420","source":"Crossref","is-referenced-by-count":141,"title":["Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools"],"prefix":"10.1093","volume":"21","author":[{"given":"Ran","family":"Su","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0697-9419","authenticated-orcid":false,"given":"Balachandran","family":"Manavalan","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"2020080709262591900_ref1","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.addr.2007.09.003","article-title":"Predicting cell-penetrating peptides","volume":"60","author":"Hansen","year":"2008","journal-title":"Adv Drug Deliv Rev"},{"key":"2020080709262591900_ref2","volume-title":"Cell-penetrating peptides and bioactive cargoes: strategies and mechanisms.","author":"Kilk","year":"2004"},{"key":"2020080709262591900_ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/414729","article-title":"Mechanisms of cellular uptake of cell-penetrating peptides","volume":"2011","author":"Madani","year":"2011","journal-title":"J Biophys"},{"key":"2020080709262591900_ref4","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.drudis.2012.03.002","article-title":"Cell-penetrating peptides: classes, origin, and current landscape","volume":"17","author":"Milletti","year":"2012","journal-title":"Drug Disco Today"},{"key":"2020080709262591900_ref5","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.molmed.2015.06.005","article-title":"Cell-penetrating peptides: strategies for anticancer treatment","volume":"21","author":"Raucher","year":"2015","journal-title":"Trends Mol Med"},{"key":"2020080709262591900_ref6","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10989-005-9393-1","article-title":"Prediction of cell-penetrating peptides","volume":"11","author":"H\u00e4llbrink","year":"2005","journal-title":"Int J Pept Res Ther"},{"key":"2020080709262591900_ref7","doi-asserted-by":"crossref","first-page":"e1002101","DOI":"10.1371\/journal.pcbi.1002101","article-title":"Prediction of cell penetrating peptides by support vector machines","volume":"7","author":"Sanders","year":"2011","journal-title":"PLoS Comput Biol"},{"key":"2020080709262591900_ref8","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1021\/acscentsci.8b00098","article-title":"Machine learning to predict cell-penetrating peptides for antisense delivery","volume":"4","author":"Wolfe","year":"2018","journal-title":"ACS Cent Sci"},{"key":"2020080709262591900_ref9","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1111\/j.1476-5381.2009.00057.x","article-title":"Twenty years of cell-penetrating peptides: from molecular mechanisms to therapeutics","volume":"157","author":"Heitz","year":"2009","journal-title":"Br J Pharmacol"},{"key":"2020080709262591900_ref10","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/0092-8674(88)90263-2","article-title":"Cellular uptake of the tat protein from human immunodeficiency virus","volume":"55","author":"Frankel","year":"1988","journal-title":"Cell"},{"key":"2020080709262591900_ref11","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bby091","article-title":"CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning. 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