{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T09:00:31Z","timestamp":1784883631717,"version":"3.55.0"},"reference-count":18,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"1","content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:p>\n                    Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or function. Two main forms are recognized: type 1 diabetes, which involves the autoimmune destruction of insulin-producing\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    -cells, and type 2 diabetes (T2D), which arises from insulin resistance and progressive\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    -cell dysfunction. Understanding the molecular mechanisms underlying these diseases is essential for the development of improved therapeutic strategies, particularly those targeting\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    -cell dysfunction. To investigate these mechanisms in a controlled and biologically interpretable setting, mouse models have played a central role in diabetes research. Owing to their genetic and physiological similarity to humans, together with the ability to precisely manipulate their genome, mice enable detailed investigation of disease progression and gene function. In particular, mouse models have provided critical insights into\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    -cell development, cellular heterogeneity, and functional failure under diabetic conditions. Building on these experimental advances, this study applies machine learning methods to single-cell transcriptomic data from mouse pancreatic islets. Specifically, we evaluate 2 supervised approaches identified in the literature, extra trees classifier and partial least squares discriminant analysis, to assess their ability to identify T2D-associated gene expression signatures at a single-cell resolution. Model performance is evaluated using standard classification metrics, with an emphasis on interpretability and biological relevance.\n                  <\/jats:p>","DOI":"10.34133\/csbj.0114","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:47:00Z","timestamp":1777448820000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Diabetic and Healthy\n                    <i>\u03b2<\/i>\n                    -Cells in Type 2 Diabetes Using Machine Learning on Single-Cell RNA Sequencing Data"],"prefix":"10.34133","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3006-9361","authenticated-orcid":true,"given":"Daniel F. O.","family":"Onah","sequence":"first","affiliation":[{"name":"Department of Information Studies, \rUniversity College London, London, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda De La Luz","family":"Lomboy Toledo","sequence":"additional","affiliation":[{"name":"Department of Information Studies, \rUniversity College London, London, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42255-023-00876-x"},{"issue":"11","key":"e_1_3_4_3_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2022.105366","article-title":"Single-cell RNA-seq transcriptomic landscape of human and mouse islets and pathological alterations of diabetes","volume":"25","author":"Chen K","year":"2022","unstructured":"Chen K, Zhang J, Huang Y, Tian X, Yang Y, Dong A. Single-cell RNA-seq transcriptomic landscape of human and mouse islets and pathological alterations of diabetes. iScience. 2022;25(11): Article 105366.","journal-title":"iScience"},{"issue":"11","key":"e_1_3_4_4_2","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1101\/pdb.top084970","article-title":"RNA sequencing and analysis","volume":"2015","author":"Kukurba KR","year":"2015","unstructured":"Kukurba KR, Montgomery SB. RNA sequencing and analysis. Cold Spring Harb Protoc. 2015;2015(11):951\u2013969.","journal-title":"Cold Spring Harb Protoc"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2022.890901","article-title":"Identification of type 2 diabetes biomarkers from mixed single-cell sequencing data with feature selection methods","volume":"10","author":"Li Z","year":"2022","unstructured":"Li Z, Pan X, Cai Y-D. Identification of type 2 diabetes biomarkers from mixed single-cell sequencing data with feature selection methods. 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A review of variable selection methods in partial least squares regression. Chemom Intell Lab Syst. 2012;118:62\u201369.","journal-title":"Chemom Intell Lab Syst"},{"issue":"1","key":"e_1_3_4_10_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/cem.2762","article-title":"The diversity in the applications of partial least squares: An overview","volume":"30","author":"Mehmood T","year":"2016","unstructured":"Mehmood T, Ahmed B. The diversity in the applications of partial least squares: An overview. J Chemometrics. 2016;30(1):4\u201317.","journal-title":"J Chemometrics"},{"issue":"1","key":"e_1_3_4_11_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s43067-023-00074-5","article-title":"Diabetes type 2 classification using machine learning algorithms with up-sampling technique","volume":"10","author":"Hama Saeed MA","year":"2023","unstructured":"Hama Saeed MA. Diabetes type 2 classification using machine learning algorithms with up-sampling technique. 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