Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.

Purpose/Significance To elucidate the complex relationship among face phenotype-disease-gene,and to explore the underlying mechanisms of their interactions.Method/Process Based on scientific literature from the PubMed database,natural language processing tools and manual filtering methods are used to extract the knowledge of the concept and relationship of face phenotype-disease-gene reported in the existing literature,and a knowledge base is constructed.Result/Conclusion The study completes the framework design and construction of the knowledge base of the face phenotype-disease-genotype for intelligent health application,which lays a foundation of both data and theory for exploring the interplay between face phenotype-disease-gene,as well as the potential application of face phenotype in disease diagnosis.