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Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved.<\/jats:p>\n               <jats:p>In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization. First, the preprocessed Dataset 1 and Dataset 2 are transformed into the form of images. The proposed MDNDO\u2013SMDU resampling technique is then used to balance the number of samples in each category in the training dataset. Finally, deep transfer learning was used to construct the predictive model MSlocPRED to identify subcellular localization for 16 classes (Dataset 1) and 18 classes (Dataset 2). The results of comparative tests of different resampling techniques show that the resampling technique proposed in this study is more effective in preprocessing for subcellular localization. The prediction results of the datasets constructed by intercepting different NC end (Both the 5' and 3' untranslated regions that flank the protein-coding sequence and influence mRNA function without encoding proteins themselves.) lengths show that for Dataset 1 and Dataset 2, the prediction performance is best when the NC end is intercepted by 35 nucleotides, respectively. The results of both independent testing and five-fold cross-validation comparisons with established prediction tools show that MSlocPRED is significantly better than established tools for identifying multi-label mRNA subcellular localization. Additionally, to understand how the MSlocPRED model works during the prediction process, SHapley Additive exPlanations was used to explain it. The predictive model and associated datasets are available on the following github: https:\/\/github.com\/ZBYnb1\/MSlocPRED\/tree\/main.<\/jats:p>","DOI":"10.1093\/bib\/bbae504","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T15:27:13Z","timestamp":1728919633000},"source":"Crossref","is-referenced-by-count":5,"title":["MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4021-3836","authenticated-orcid":false,"given":"Yun","family":"Zuo","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University , No. 1800 Lihu Avenue, Binhu District, Wuxi 214000 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bangyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University , No. 1800 Lihu Avenue, Binhu District, Wuxi 214000 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2452-7580","authenticated-orcid":false,"given":"Wenying","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology , 5340 Xiping Road, Beichen District, Tianjin 300130 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University , Wellington Rd, Clayton VIC 3800 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9885-1978","authenticated-orcid":false,"given":"Xiangrong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology , National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Intelligent Storage and Computing, , 422 Siming South Road, Siming District, Xiamen City, Fujian 361005 ,","place":["China"]},{"name":"Xiamen University , National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Intelligent Storage and Computing, , 422 Siming South Road, Siming District, Xiamen City, Fujian 361005 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Hunan University , Yuelu District, Changsha 410012 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-2426","authenticated-orcid":false,"given":"Zhaohong","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, 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