Computational immunogenomics: Leveraging AI to uncover novel biomarkers for disease diagnosis and therapy

Le résumé fourni par la source

In a groundbreaking fusion of Artificial Intelligence (AI) and immunogenomics, the quest for precision medicine is being redefined, unlocking novel biomarkers that revolutionize disease diagnosis and therapy. This review unveils how AI harnesses the power of multi-omics data genomics, transcriptomics, and proteomics to decode the immune system’s complexities, spotlighting breakthroughs in cancer, autoimmune disorders, and infectious diseases. Cutting-edge machine learning, deep learning, and foundation models like scGPT illuminate immune signatures, enabling early detection of lung cancer, tailored therapies for rheumatoid arthritis, and rapid vaccine development for pandemics. From single-cell RNA sequencing to graph neural networks, AI’s analytical prowess transforms vast datasets into actionable insights, driving personalized healthcare. Yet, challenges loom: data integration complexities, algorithmic biases, ethical concerns, and regulatory hurdles threaten clinical translation. Future innovations, including quantum computing and global data-sharing frameworks, promise to surmount these barriers, heralding a new era of equitable, precise medicine. Synthesizing insights from the recent five years, this article captivates with its vision of AI-driven immunogenomics as a beacon for healthcare transformation, urging researchers, clinicians, and policymakers to unite in overcoming obstacles and realizing a future where personalized diagnostics and therapies conquer disease with unprecedented precision.

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