Integrated computational modeling of multi-scale biomedical data

多尺度生物医学数据的集成计算建模

• 批准号: RGPIN-2020-05987
• 负责人: Simpson, Amber
• 金额: $ 2.11万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750630
• 关键词: Integrated; computational; modeling; multi; scale

Pancreas cancer is a uniformly deadly disease due to the late stage at diagnosis and the lack of effective therapies. The proposed research program will develop new methods to analyze and integrate biomedical data at multiple scales to better elucidate the underlying mechanisms of pancreatic cancer where mutational analysis alone fails to define complete biological landscapes. The proposed program aims to bridge this critical barrier by integrating omics-based analyses of diagnostic imaging, histopathology slides, genomic data, and other sources to provide a unified, cogent approach to pancreatic cancer characterization. Substantial advances in machine learning algorithms combined with improvements in the resolution of diagnostic imaging, routine digitization of microscopy slides, and the influx of next generation sequencing has created an unrivaled opportunity that this program exploits. Diagnostic imaging, molecular profiling, and histologic imaging create a more complete biological picture than any individual modality. Our long-term goal is to harness data to change the way that patients are treated, utilizing novel computational methods to realize the promise of precision medicine - the greatest challenge in modern cancer care. Training of highly qualified personnel in this program will include experience in machine learning, biomedical data science, and computational modeling. Five PhD, three Master's, and four undergraduate students will receive training in the proposed program. HQP trained in machine learning and biomedical data science are in high demand in Canada and trainees will play a critical role in accelerating state-of-the-art approaches that will radically transform care for cancer patients by informing individualized treatments, at a reduced cost to Canadian healthcare.

胰腺癌是一种普遍致命的疾病，其原因是诊断较晚，且缺乏有效的治疗方法。拟议的研究计划将开发新的方法来分析和整合多个尺度的生物医学数据，以更好地阐明胰腺癌的潜在机制，在胰腺癌中，仅靠突变分析无法定义完整的生物景观。拟议的计划旨在通过整合基于组学的诊断成像、组织病理学切片、基因组数据和其他来源的分析来弥合这一关键障碍，以提供统一、令人信服的方法来表征胰腺癌。机器学习算法的重大进步，加上诊断成像分辨率的提高，显微镜载玻片的常规数字化，以及下一代测序的涌入，创造了这个程序所利用的无与伦比的机会。诊断成像、分子成像和组织成像比任何个体成像都能创造出更完整的生物学图景。我们的长期目标是利用数据来改变患者的治疗方式，利用新的计算方法来实现精确医学的前景--这是现代癌症护理中最大的挑战。对高素质人才的培训将包括机器学习、生物医学数据科学和计算建模方面的经验。五名博士、三名硕士和四名本科生将在拟议的计划中接受培训。在加拿大，受过机器学习和生物医学数据科学培训的HQP需求很大，受训人员将在加速最先进的方法方面发挥关键作用，这些方法将通过告知个性化治疗来从根本上改变对癌症患者的护理，同时降低加拿大医疗保健的成本。