{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T12:04:07Z","timestamp":1783598647184,"version":"3.55.0"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF1000100"],"award-info":[{"award-number":["2022YFF1000100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The webserver and source code are freely available at http:\/\/bigdata.biocie.cn\/CTISLweb\/home and https:\/\/zenodo.org\/records\/10568906, respectively.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae063","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T04:26:57Z","timestamp":1707193617000},"source":"Crossref","is-referenced-by-count":6,"title":["CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data"],"prefix":"10.1093","volume":"40","author":[{"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Software Engineering, College of Information Engineering, Northwest A&F University , Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Chai","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Information Engineering, Northwest A&F University , Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohua","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Information Engineering, Northwest A&F University , Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-3655","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1847-754X","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Monash University , Melbourne, VIC 3800, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4821-3585","authenticated-orcid":false,"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Northwest A&F University , Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanzhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Information Engineering, Northwest A&F University , Yangling 712100, China"},{"name":"Shaanxi Engineering Research Center of 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