Cancer Research Workforce Development in FAIR Artificial Intelligence and Machine Learning

FAIR 人工智能和机器学习中的癌症研究劳动力发展

• 批准号: 10405929
• 负责人: William Douglas Cress
• 金额: $ 8.63万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405929
• 关键词: Artificial Intelligence; Machine Learning; Workforce Development; anticancer research

Given the rapidly growing collection of clinical and molecular data available for cancer and other diseases, the potential application of artificial intelligence (AI) and machine learning (ML) approaches applied better and more cost-effective clinical decision making is compelling. However, the success of the application of AI/ML algorithms, especially in the clinical domain, hinges on the availability and quality of data for training and validation of the AI/ML models. Therefore, an unmet need is in the development of competencies and skills needed to make biomedical data ready for AI/ML applications that can meet the four FAIR requirements: Findable, Accessible, Interoperable, and Reusable. This supplement to our Integrated Program in Cancer Data Science addresses this need by developing a short course in FAIR application that will be distributed in three venues: 1) a short-course for Ph.D student offer through the University of South Florida, which host our Cancer Biology PhD Program, 2) hands-on workshops for postdoctoral fellows and other early-staged investigator at the Moffitt Cancer Center, and 3) public dissemination of videotaped lectures via Moffitt YouTube channel.

鉴于可用于癌症和其他的临床和分子数据的快速增长 疾病，人工智能（AI）的潜在应用和机器学习（ML） 采用更好，更具成本效益的临床决策的方法令人信服。 但是，AI/ML算法的应用成功，尤其是在临床领域中 取决于AI/ML模型的培训和验证数据的可用性和质量。 因此，未满足的需求是发展能力和技能 生物医学数据准备适用于可以满足四个公平要求的AI/ML应用程序： 可找到，可访问，可互操作和可重复使用。这补充了我们的综合计划 在癌症数据科学中，通过在公平应用中开发短课程来满足这一需求 这将在三个场所分发：1）通过 南佛罗里达大学主持我们的癌症生物学博士学位课程，2）动手 博士后研究员和莫菲特癌的其他早期研究员的讲习班 中心和3）通过Moffitt YouTube频道公开传播录像的演讲。