Towards Integrative Data Analysis for Predictive Modeling in Biomedical Computing

生物医学计算中预测建模的综合数据分析

• 批准号: RGPIN-2015-06051
• 负责人: Mousavi, Parvin
• 金额: $ 3.13万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688731
• 关键词: Towards; Integrative; Data; Analysis; Predictive

This research is aimed at developing innovative methods to analyze medical data and understand the mechanisms of actions of complex disease, specifically prostate cancer (PCa). The new era of large-scale biology is characterized by insurmountable amounts of data including medical images, high throughput genetic information, large public databases, and clinical signals. Data abundance stands in contrast to our limited knowledge about the biological events underlying disease. The proposed research seeks to close this gap through a novel framework that uses state-of-the-art image analysis methods, and integrates molecular data and biological information, to formulate more accurate models to characterize cancer.****Although PCa is the third leading cancer-related cause of death in North American men, if diagnosed early, it can be managed with a 90% long-term disease free survival rate. Diagnosis of PCa requires biopsy, guided by ultrasound. However, ultrasound guidance is not tailored to an individual and has poor sensitivity in detection of cancer, specifically its aggressive forms. There is an urgent need to clearly distinguish between aggressive PCa that requires intervention and indolent PCa that can be put on "watchful waiting". The algorithms developed here will address this challenge by i) augmenting ultrasound images with information from probability maps indicating the aggressiveness of cancer, ii) comprehensive analysis of molecular information to stratify disease outcome; and iii) integration of features at multiple resolutions of data to build a comprehensive model of PCa.****The unparalleled methods for acquisition and analysis of ultrasound signals provide enhanced prediction of the existence and extent of PCa. The innovative models formulated by integration of features of multiple modalities facilitate the discovery of personalized therapies and biopsies. The methods and software built will help the clinicians make better management decisions for PCa to reduce its mortality while balancing it against the risks of unnecessary overtreatment. The computational methods sought will advance research in image and signal processing, machine learning, systems biology and software engineering. Methods are discussed in the framework of Pca; however, they are extendible to other disorders. ****Training of highly qualified personnel is a primary focus of this work; three PhD and three Master's students, as well as five summer undergraduates will be involved in this research program.**

本研究旨在开发创新的方法来分析医学数据和了解复杂疾病的作用机制，特别是前列腺癌（PCa）。大规模生物学的新时代的特点是不可逾越的数据量，包括医学图像、高通量遗传信息、大型公共数据库和临床信号。数据的丰富与我们对潜在疾病的生物学事件的有限知识形成对比。拟议的研究旨在通过一个新的框架来缩小这一差距，该框架使用最先进的图像分析方法，并整合分子数据和生物信息，以制定更准确的模型来表征癌症。****虽然前列腺癌是北美男性死亡的第三大癌症相关原因，但如果早期诊断，它可以获得90%的长期无病生存率。前列腺癌的诊断需要在超声引导下进行活检。然而，超声引导并不是为个人量身定制的，而且在检测癌症，特别是其侵袭性形式方面的敏感性很差。迫切需要明确区分需要干预的侵袭性前列腺癌和可以“观察等待”的惰性前列腺癌。本文开发的算法将通过以下方式解决这一挑战：i)利用表明癌症侵袭性的概率图信息增强超声图像；ii)对分子信息进行综合分析，对疾病结果进行分层；iii)整合多分辨率数据的特征，构建PCa综合模型。****无与伦比的超声信号采集和分析方法为PCa的存在和程度提供了增强的预测。通过整合多种模式的特点制定的创新模型促进了个性化治疗和活检的发现。所建立的方法和软件将帮助临床医生对PCa做出更好的管理决策，以降低其死亡率，同时平衡其与不必要的过度治疗的风险。所寻求的计算方法将推进图像和信号处理、机器学习、系统生物学和软件工程的研究。在主成分分析的框架下讨论了方法；然而，它们可以扩展到其他疾病。****培训高素质人员是这项工作的主要重点；本研究项目将招收博士生3名、硕士生3名、暑期本科生5名