Artificial Intelligence in Lipidomics: Advancing Biomarker Discovery, Pathway Analysis, and Precision Medicine
Mayyas Al‐Remawi, Faisal Aburub, Faisal Al‐Akayleh, Rami A. Abdel‐Rahem, Ahmed S.A. Ali Agha
Abstract
Lipidomics, a branch of metabolomics, provides critical insights into cellular processes and disease mechanisms by profiling lipid species in biological systems. Despite its potential, traditional lipidomics faces significant challenges, including high-dimensional data, variability across experiments, and difficulties integrating with other omics layers. Artificial intelligence (AI) has emerged as a transformative tool, leveraging machine learning and deep learning algorithms to overcome these limitations. AI enables precise lipid biomarker discovery for diseases such as Alzheimer's, diabetes, and cancer, facilitates the elucidation of lipid metabolic pathways, and accelerates drug discovery through improved target identification and pharmacokinetics. Additionally, AI-driven lipidomics supports personalized medicine by tailoring therapeutic strategies based on individual lipidomic profiles and advances environmental research by identifying lipid alterations under stress conditions. However, challenges such as data heterogeneity, lack of standardization, and limited interpretability of AI models remain. Future advancements, including real-time AI-powered lipidomics, federated learning for collaborative research, and ethical frameworks, promise to address these barriers. The field is poised to revolutionize systems biology and precision medicine by integrating lipidomics with cutting-edge AI methodologies, offering profound implications for diagnostics, therapeutics, and environmental health.