CAREER: Advancing Shape Learning for Biosciences

职业：推进生物科学的形状学习

• 批准号: 2240158
• 负责人: Nina Miolane
• 金额: $ 49.64万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240158&HistoricalAwards=false
• 关键词: CAREER; Advancing; Shape; Learning; Biosciences

Understanding the healthy and pathological shapes of biological structures (proteins, cells, organs) directly from image data is critical to understand their roles in living organisms. The impact for human health and society range from our understanding of cancers to the diagnosis of neurodegenerative diseases. This CAREER proposal will evaluate and develop reliable shape analysis methods that can harness the recent bio-imaging data explosion, advance our understanding of the fundamental rules of life, and enable breakthroughs in data-driven biomedicine. Tightly integrated with the research activities, the education and outreach objective is to engage diverse audiences in shape analysis and bioscience through novel art-science performances for high-school students, pioneering courses on geometric machine learning for shape analysis, training of graduate students, and free community outreach lectures for the wide audience.Despite impressive advances in the field of shape analysis, its deployment to biosciences is prohibited by computational and statistical hurdles. This yields challenges related to the interpretation of results, where inconsistent analyses bear the danger of driving scientific conclusions in the wrong direction —a serious drawback for a discipline that ultimately researches human health. In mathematics, (biological) shapes can be represented as shapes of key points, shapes of curves, or shapes of surfaces. The associated shape data spaces present common abstract geometric structures of non-Euclidean manifolds. This project will utilize these commonalities to establish a consistent numerical framework to systematically and exhaustively evaluate the possible inconsistencies of machine learning algorithms on shape spaces. In particular, it will provide a deep dive into the geodesic and polynomial regression models on non-Euclidean manifolds. The findings will be leveraged into a pilot study that will reliably extract biologically relevant parameters on the morphodynamics of cells migrating in vivo.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解生物结构的健康和病理形状（蛋白质，细胞，器官）以及对人类健康和社会的影响，从我们对癌症的理解到诊断神经化的诊断，该职业将评估分析方法。数据漏洞与研究紧密地融合在一起，通过新颖的艺术科学研究，对形状分析和生物科学进行了多样化，尽管令人印象深刻的是，但对研究生的培训在形状分析的领域中，他与结果的解释相关的是，不一致的分析是朝着方向推动科学结论的危险 - 这是对最终人类健康的学科的严重缺点形状数据空间呈现非欧盟歧管的常见抽象几何结构。研究结果将其定为一项试验性研究，该研究对细胞的形态动力学有意义的参数。这一奖项反映了NSF的表现，并且值得使用Toundation IT和更广泛的影响评估标准。