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Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM\u2013DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code and data can be downloaded from https:\/\/github.com\/Excelsior511\/StackCPPred.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa131","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T20:16:28Z","timestamp":1582661788000},"page":"3028-3034","source":"Crossref","is-referenced-by-count":133,"title":["StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-2573","authenticated-orcid":false,"given":"Xiangzheng","family":"Fu","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, Changsha, Hunan 410082, China"}]},{"given":"Lijun","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, Changsha, Hunan 410082, China"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, Changsha, Hunan 410082, China"}]},{"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 610054, China"}]}],"member":"286","published-online":{"date-parts":[[2020,2,27]]},"reference":[{"key":"2023013111544626200_btaa131-B1","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.sbi.2011.03.011","article-title":"Intrinsically disordered proteins: regulation and disease","volume":"21","author":"Babu","year":"2011","journal-title":"Curr. 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