Utilizing AI-Enhanced Multi-Omics Integration for Predictive Modeling of Disease Susceptibility in Functional Phenotypes - Zhou, Yanlin; Shen, Xinyu; He, Zheng; Weng, Huiying; Chen, Wangmei (Journal of Theory and Practice of Engineering Science, 2024)
With the continuous development of machine learning technology, the scientific research of biomedical materials is gradually shifting to a data-driven direction. The rise of this trend stems from the widespread use of Bio sequencing technology, which provides entirely new methods and insights for testing and evaluating the biological function of biomedical materials. The performance and performance of biomedical materials have a wide range of applications in medical applications, drug delivery, biosensors and other fields, so it is important to further optimize them. However, with the accumulation and increasing complexity of data, there is a need for more intelligent and efficient ways to process and analyze this heterogeneous scientific data. Therefore, the establishment of an open, shared infrastructure for storing heterogeneous scientific data from different research fields will be the cornerstone of cross-disciplinary joint analysis. This infrastructure will not only accelerate the collection and integration of data, but will also provide opportunities for collaboration and innovation across disciplines. This paper highlights a new trend in biomedical materials research, namely a data-driven approach, and the key role of Bio sequencing technology in this process. At the same time, we call for the establishment of an open data storage and sharing platform to promote multidisciplinary cooperation, accelerate the optimization and innovation of biomedical materials, and open up broader prospects for future biomedical applications. This effort is expected to push scientific research in the medical field to new heights, providing safer and more effective treatments and medical programs for patients.