Neural Networks of Knowledge: Ontologies Pioneering Precision Medicine in Neurodegenerative Diseases

The review focuses on the ways that ontologies are revolutionising precision medicine in their effort to understand neurodegenerative illnesses. Ontologies, which are structured frameworks that outline the relationships between concepts in a certain field, offer a crucial foundation for combining different biological data. Novel insights into the construction of a precision medicine approach to treat neurodegenerative diseases (NDDs) are given by growing advancements in the area of pharmacogenomics. Affected parts of the central nervous system may develop neurological disorders, including Alzheimer's, Parkinson's, autism spectrum, and attention-deficit/hyperactivity disorder. These models allow for standard and helpful data marking, which is needed for cross-disciplinary study and teamwork. With case studies, you can see how ontologies have been used to find biomarkers, understand how sicknesses work, and make models for predicting how drugs will work and how the disease will get worse. For example, problems with data quality, meaning variety, and the need for constant changes to reflect the growing body of scientific knowledge are discussed in this review. It also looks at how semantic data can be mixed with cutting-edge computer methods such as artificial intelligence and machine learning to make brain disease diagnostic and prediction models more exact and accurate. These collaborative networks aim to identify patients at risk, identify patients in the preclinical or early stages of illness, and develop tailored preventative interventions to enhance patient quality of life and prognosis. They also seek to identify new, robust, and effective methods for these patient identification tasks. To this end, the current study has been considered to examine the essential components that may be part of precise and tailored therapy plans used for neurodegenerative illnesses.