Abstract:

The high-throughput sequencing technology of genomic DNA has greatly improved the diagnostic efficiency of rare diseases, but currently there are still some patients who have not been diagnosed. In recent years, various detection techniques such as transcriptome, proteomics, metabolomics and lipidomics, and epigenetics have gradually been applied in clinical practice, making it possible to comprehensively diagnose rare disease patients based on these multiomics methods. On the other hand, with the increase of confirmed cases, how to effectively integrate patient clinical data and build rare disease databases to meet the construction needs of high-quality research-oriented patient cohorts has become an increasingly important issue for governments around the world. More importantly, the development of big data models that integrate multiomics information can promote the application of artificial intelligence and machine learning in rare disease research. This will contribute to the clinical evaluation and precise classification of rare disease patients, and effectively improve the research and development efficiency of innovative diagnostic and therapeutic technologies such as gene therapy. Rare disease research has entered the era of digital medicine, which is also a practical need to meet the precise diagnosis and personalized treatment of patients at a higher level.

Key words: rare disease, multiomics techniques, database construction, diagnosis and treatment, digital medicine