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They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed. Although several models for identifying enhancers and their strengths have been proposed, their accuracy and efficiency have yet to be improved.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a two-layer predictor called \u2018iEnhancer-XG.\u2019 It comprises a one-layer predictor (for identifying enhancers) and a second classifier (for their strength) and uses \u2018XGBoost\u2019 as a base classifier and five feature extraction methods, namely, k-Spectrum Profile, Mismatch k-tuple, Subsequence Profile, Position-specific scoring matrix (PSSM) and Pseudo dinucleotide composition (PseDNC). Each method has an independent output. We place the feature vector matrix into the ensemble learning for fusion. This experiment involves the method of \u2018SHapley Additive explanations\u2019 to provide interpretability for the previous black box machine learning methods and improve their credibility. The accuracies of the ensemble learning method are 0.811 (first layer) and 0.657 (second layer). The rigorous 10-fold cross-validation confirms that the proposed method is significantly better than existing technologies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code and dataset for the enhancer predictions have been uploaded to https:\/\/github.com\/jimmyrate\/ienhancer-xg.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa914","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T19:38:53Z","timestamp":1602790733000},"page":"1060-1067","source":"Crossref","is-referenced-by-count":88,"title":["iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor"],"prefix":"10.1093","volume":"37","author":[{"given":"Lijun","family":"Cai","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , 410082 Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanbai","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , 410082 Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-2573","authenticated-orcid":false,"given":"Xiangzheng","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , 410082 Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Hunan University of Science and Technology , 411103 XiangTan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , 410082 Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , 410082 Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"2023051612064661000_btaa914-B1","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1186\/s12859-017-1828-0","article-title":"A new method for enhancer prediction based on deep belief network","volume":"18","author":"Bu","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023051612064661000_btaa914-B2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s00726-006-0485-9","article-title":"Prediction of linear B-cell epitopes using amino acid pair antigenicity scale","volume":"33","author":"Chen","year":"2007","journal-title":"Amino Acids"},{"key":"2023051612064661000_btaa914-B3","first-page":"785","author":"Chen","year":"2016"},{"key":"2023051612064661000_btaa914-B4","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ab.2014.04.001","article-title":"PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition","volume":"456","author":"Chen","year":"2014","journal-title":"Anal. 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