The role of machine learning in discovering biomarkers and predicting treatment strategies for neurodegenerative diseases: A narrative review

Abstract

Machine learning in the field of computational intelligence continues to increase significantly and shows potential in the identification, management, treatment, and monitoring of complex diseases, such as neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. Although there are currently no conclusive diagnostic and therapeutic approaches for neurodegenerative diseases, scientists have adopted machine learning algorithms alongside neuroimaging technology to examine significant symptoms and mutations in neurodegenerative diseases. Deep learning algorithms, including neural networks, have shown the ability to monitor brain structure and physiology alterations associated with infections, patients’ motor and cognitive symptoms, and their reactions to treatment. This narrative review article provides insight into the critical role of machine learning in neurodegenerative diseases, particularly in biomarker discovery and prediction of therapeutic strategies. Neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease, are characterized by an abnormal accumulation of specific proteins in the brain, and these protein aggregates are believed to be the main cause of neurotoxicity and neuronal dysfunction. The article focuses on how machine learning can identify disease-related biomarkers and potential therapeutic targets by analyzing protein interaction and mutation data, emphasizing the importance of biomarker discovery in early diagnosis and disease progression monitoring. The utilization of machine learning has the potential to expedite drug discovery, pinpoint novel biomarkers, and tailor personalized treatment plans for individuals afflicted with neurodegenerative conditions. This advancement can enhance the precision of disease diagnosis and therapy, leading to improved clinical outcomes for individuals.