Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data Subject The topic of the resource AChOA; Gene; Monte Carlo; ODBN; scRNA-seq; Type 2 diabetes Description An account of the resource Diabetes is a critical disease and is crucial to personage agility. Type 2 Diabetes (T2D) accounts for 92% of epithetical cases. This paper proposes an optimized type 2 diabetes detection model using mixed single-cell RNA sequencing (scRNA-seq) technology. Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Early detection of the disease can greatly improve treatment outcomes, but current diagnostic methods have limitations. Our proposed model integrates scRNA-seq data from both human pancreatic beta cells to identify gene expression patterns associated with diabetes. Our study shows that the proposed model is highly accurate in identifying diabetes, achieving an area under the curve (AUC) of 0.98. We employed an optimized model to improve the detection of diabetes at an early stage, leading to better treatment outcomes and an improved quality of life for patients. We initially incorporated optimal features from the dataset using the Monte Carlo (MC) feature selection method. This method helped us to estimate the relative importance (RI) score of each gene or feature, which is then used to rank the features. Further, we proposed an optimized deep belief network (ODBN) as a classification model to classify T2D and non-diabetes. To improve the performance of ODBN, an adaptive chimp optimization algorithm (AChOA) is introduced to optimize the weight parameters and achieved a performance accuracy of 96.57%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Padmaja K.; Mukhopadhyay D. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 6 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-023-02215-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02215-z</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173921389&amp;doi=10.1007%2Fs42979-023-02215-z&amp;partnerID=40&amp;md5=8b455e35c33da718ba9a467a19992151" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173921389&amp;doi=10.1007%2fs42979-023-02215-z&amp;partnerID=40&amp;md5=8b455e35c33da718ba9a467a19992151</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Padmaja K., Department of CSE, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Mukhopadhyay D., Department of CSE, CHRIST (Deemed to be University), Karnataka, Bangalore, India