Bioscience Evidence 2024, Vol.14, No.1, 16-23 http://bioscipublisher.com/index.php/be 20 Bluetooth connection, these devices input data into the application. In addition, Breathe RM also integrates data from personal smartwatches, and patients actively contribute to the data pool by self-reported cough frequency and overall health status. This comprehensive approach ensures a comprehensive understanding of the patient's condition (https://www.cryptopolitan.com/ai-powered-app-10-days-cystic-fibrosis/). 3.2 The role of data and algorithms Behind successful cases, the role of data and algorithms cannot be ignored. In the discovery of rare disease drugs, the accessibility and quality of data are crucial for the application of AI. Large scale genomic data, clinical data, and drug databases provide AI with rich information sources that can reveal the etiology, biological pathways, and drug targets of rare diseases. The selection and optimization of AI algorithms are also key to success. The application of machine learning and deep learning algorithms can process complex biological data, mine hidden patterns and correlations (Zheng et al., 2019). The continuous optimization and iteration of algorithms enable AI models to more accurately predict the efficacy, side effects, and safety of drugs. Therefore, the full utilization of data and algorithms is an important factor in promoting the discovery of rare disease drugs. The successful case of Breathe RM suggests that this AI driven application may become a broader model for disease management. It can improve the quality of life of patients, reduce unnecessary visits and medical burden. In addition, this technology also provides valuable data resources for researchers to further understand the development and management of cystic fibrosis and other respiratory diseases. 3.3 The value of interdisciplinary cooperation The discovery of rare disease drugs requires interdisciplinary collaboration, and AI technology provides a platform for collaboration and communication among experts from different fields. The collaboration between biologists, doctors, pharmaceutical chemists, and data scientists can bring together their professional knowledge and skills to address the challenges in rare disease drug discovery (Bendowska and Baum, 2023). As a tool and bridge, AI can integrate data and knowledge from different fields, accelerate information sharing and communication. Through interdisciplinary collaboration, researchers can gain a more comprehensive understanding of the characteristics and mechanisms of rare diseases, and design more effective drug strategies. Interdisciplinary collaboration has important value in promoting the discovery of rare disease drugs, and AI, as a collaborative tool, provides support and assistance for expert collaboration in different fields. The case studies of AI in promoting the discovery of rare disease drugs have demonstrated its enormous potential. The role of data and algorithms is crucial for successful cases, providing a rich source of information and optimized models. At the same time, interdisciplinary cooperation is also key to promoting the discovery of rare disease drugs. AI, as a collaborative tool, promotes communication and cooperation among experts in different fields. These case studies provide new therapeutic hope for patients with rare diseases and lay the foundation for future research and development. 4 Challenges and Prospects 4.1 Technical and data challenges In the process of promoting the discovery of rare disease drugs, technology and data are one of the main challenges (Nestler-Parr et al., 2018). Rare diseases often have complex genetic foundations, thus requiring highly accurate molecular diagnosis and personalized treatment methods. However, existing technological tools still have limitations in dealing with this complexity. Further development and improvement are needed in high-throughput sequencing technology, bioinformatics tools, and data analysis algorithms to better understand the genetic mechanisms of rare diseases. Due to the small number of rare disease patients, data collection and sharing also face challenges. In the process of traditional drug discovery, large-scale sample data is crucial for establishing accurate models and predictions. Groft et al. (2021) found that the limited number of patients with rare diseases makes data collection difficult. It is necessary to establish a global collaborative network to promote data sharing and collaborative research, expand the scale of rare disease datasets, and improve the accuracy of disease prediction and drug discovery.
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