Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data

• URL: http://arxiv.org/abs/2602.09036v1
• Date: Fri, 30 Jan 2026 03:27:06 GMT
• Title: Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data
• Authors: Maria De La Luz Lomboy Toledo, Daniel Onah,
• Abstract summary: Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or function.<n>Two main forms are recognized: type 1 diabetes (T1D), which involves autoimmune destruction of insulin-producing beta-cells, and type 2 diabetes (T2D), which arises from insulin resistance and progressive beta-cell dysfunction.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (T1D), which involves autoimmune destruction of insulin-producing \b{eta}-cells, and type 2 diabetes (T2D), which arises from insulin resistance and progressive \b{eta}-cell dysfunction. Understanding the molecular mechanisms underlying these diseases is essential for the development of improved therapeutic strategies, particularly those targeting \b{eta}-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 \b{eta}-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 two supervised approaches identified in the literature; Extra Trees Classifier (ETC) and Partial Least Squares Discriminant Analysis (PLS-DA), to assess their ability to identify T2D-associated gene expression signatures at single-cell resolution. Model performance is evaluated using standard classification metrics, with an emphasis on interpretability and biological relevance

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