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

Transcriptomic profiling has become an essential technique in unraveling the cellular and molecular intricacies of ocular tissues. By analyzing gene expression patterns, researchers gain valuable insights into key areas such as retinal development, corneal diseases, macular degeneration, and glaucoma. The rise of advanced technologies, including microarray platforms, bulk RNA sequencing (RNA-seq), and single-cell RNA sequencing (scRNA-seq), has significantly expanded the volume and complexity of transcriptomic data. To handle this high-dimensional data effectively, artificial intelligence (AI) methods, particularly machine learning (ML) algorithms, have emerged as powerful analytical tools within ophthalmology. This review collates recent advancements from 2019 through 2025, focusing on how both unsupervised and supervised learning techniques have propelled ocular transcriptomics research forward. Unsupervised learning methods 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) have become standard procedures, especially in single-cell data workflows. These techniques enable researchers to uncover intrinsic patterns and cluster cells by type or state without prior labels, which is vital in characterizing complex eye tissues and disease mechanisms. Supervised learning approaches have also demonstrated significant utility in this field. Techniques including least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs) have been instrumental in identifying diagnostic and prognostic biomarkers across a range of ocular diseases. These diseases include age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO). By training models on labeled datasets, researchers can predict disease states or progression, enabling early diagnosis and personalized treatment strategies. Deep learning frameworks, such as variational autoencoders and neural networks, are increasingly leveraged to integrate multi-omics data, combining transcriptomic information with other molecular layers. This integration provides a more comprehensive understanding of ocular pathophysiology, facilitating precision ophthalmology. Despite these advancements, challenges remain, particularly concerning interpretability of AI models and the lack of standardized procedures across studies. The rise of explainable AI initiatives and multimodal analytical approaches offers promising solutions to these issues, supporting transparent and robust clinical translation. Overall, AI-driven transcriptomic analysis is transforming ophthalmology by enabling refined biomarker discovery, improved cell classification, and enhanced disease modeling. These developments hold great potential for accelerating research and clinical applications in eye health, fostering innovation in diagnostics and therapeutics for complex ocular conditions.