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.

直接从图像数据中了解生物结构（蛋白质，细胞，器官）的健康和病理形状对于了解其在生物组织中的作用至关重要。对人类健康和社会的影响范围从我们对癌症的理解到神经退行性疾病的诊断。该职业建议将评估和开发可靠的形状分析方法，这些方法可以利用最近的生物成像数据爆炸，促进我们对与研究活动紧密整合的基本规则的理解，教育和外展目标是，通过对高级学生的高中生，培训机器分析，在形状分析中吸引分析和生物科学的潜水性分析，并通过新颖的艺术科学表演进行培训，以实现培训，并在训练中进行培训，以培训机器的培训，并培训机器的培训，以培训机器的培训，以实现培训，以实现培训，以实现培训。尽管在形状分析领域的进步令人印象深刻，但其部署到生物科学的情况下，计算和统计障碍禁止。这产生了与结果的解释有关的挑战，在这种挑战中，不一致的分析具有朝着错误的方向推动科学结论的危险，这是对最终研究人类健康的学科的严重缺点。在数学中，（生物）形状可以表示为要点，曲线形状或表面形状的形状。相关的形状数据空间呈现非欧盟歧管的常见抽象几何结构。该项目将利用这些共同点来建立一个一致的数值框架，以系统地有效地评估形状空间上机器学习算法的可能性。特别是，它将深入研究非欧国人歧管上的大地测量和多项式回归模型。这些发现将被利用为一项试点研究，该研究将可靠地提取有关体内迁移的细胞形态学的生物学相关参数。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响审查标准，通过评估来诚实地对其进行评估。