Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders
Syed Raza Abbas, Zeeshan Abbas, Arifa Zahir, Seung Won Lee
Abstract
Precision medicine tailors medical procedures to individual genetic overviews and offers transformative solutions for rare genetic conditions. Machine learning (ML) has enhanced genome-based precision medicine (GBPM) by enabling accurate diagnoses, customized treatments, and risk assessments. ML tools, including deep learning and ensemble methods, process high-dimensional genomic data and reveal discoveries in rare diseases. This review analyzes the ML applications in GBPM, emphasizing its role in disease classification, therapeutic optimization, and biomarker discovery. Key challenges, such as computational complexity, data scarcity, and ethical concerns, are discussed alongside advancements such as hybrid ML models and real-time genomic analysis. Security issues, including data breaches and ethical challenges, are addressed. This review identifies future directions, emphasizing the need for comprehensible ML models, increasing data-sharing frameworks, and global collaborations. By integrating the current research, this study provides a comprehensive perspective on the use of ML for rare genetic disorders, paving the way for transformative advancements in precision medicine.