Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning

Authors

Catherine Lalman, Thomas Jefferson UniversityFollow
Yimin Yang
Janice L. Walker, Thomas Jefferson UniversityFollow

Document Type

Article

Publication Date

8-26-2025

Comments

This article is the author's final published version in Cells, Volume 14, Issue 17, September 2025, Article number 1315.

The published version is available at https://doi.org/10.3390/cells14171315. Copyright © 2025 by the authors.

Abstract

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. © 2025 by the authors.

Recommended Citation

Lalman, Catherine; Yang, Yimin; and Walker, Janice L., "Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning" (2025). Department of Pathology, Anatomy, and Cell Biology Faculty Papers. Paper 457.
https://jdc.jefferson.edu/pacbfp/457

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

40940727

Language

English