Component-based methods for data reduction and integration: application to genomics and neuroimaging studies.

基于组件的数据缩减和集成方法：在基因组学和神经影像研究中的应用。

• 批准号: RGPIN-2016-04919
• 负责人: Labbe, Aurelie
• 金额: $ 1.6万
• 依托单位: HEC Montréal
• 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=678626
• 关键词: Component; based; methods; data; reduction

It is now well accepted that the biggest challenges and upcoming breakthroughs in the genomic field will be derived from handling big data. Not only is big data generated faster than our own capacity to fully understand, interpret and evaluate the results produced, it is also collected from an ever-increasing number of sources. As a result, statistical challenges with integrating this information into a unified model of genome function needs to be addressed. With a particular focus on genomic studies, the proposed research endeavours to develop a set of new statistical tools designed to investigate association between two or more sets of possibly high dimensional correlated variables measured on the same subjects. In particular, the general objective of the proposal is to tackle both the issue of data reduction and association analysis simultaneously, by developing statistical models achieving an optimal data reduction towards the final goal of detecting associations between datasets. Graphical model tools, such as the ones discussed in this project, need to be carefully evaluated in the context of genomic studies. The potential of such models to uncover new complex network patterns is tremendous and recent development in this fields has led to an to increase in their computing efficiency. This proposal has also been developed in the context of the creation of a new institute the Ludmer Centre for Neuroinformatics and Mental Health at McGill. Researchers at the new Ludmer Centre engage in a special multidisciplinary research platform, with the goal of understanding how different genetic, epigenetic and environmental factors influence brain development in children. The approach combines disciplines including neuroscience, computational biology, mathematics, genetics, epigenetics, bioinformatics, epidemiology and computer science. In this context, the proposed project is very relevant and will therefore enhance direct knowledge transfer. In particular, I expect that discoveries will open new pathways for diagnosis, prevention and treatment. **

现在人们普遍认为，基因组领域最大的挑战和即将到来的突破将来自处理大数据。大数据不仅产生的速度快于我们自己完全理解、解释和评估所产生结果的能力，而且还从越来越多的来源收集。因此，需要解决将这些信息整合到基因组功能统一模型中的统计挑战。特别关注基因组研究，拟议的研究致力于开发一套新的统计工具，旨在调查两组或多组可能高维相关变量之间的关联，对相同的主题进行测量。具体而言，该提案的总体目标是同时解决数据减少和关联分析问题，方法是开发统计模型，实现最佳数据减少，最终目标是检测数据集之间的关联。图形模型工具，如本项目中讨论的，需要在基因组研究的背景下仔细评估。这种模型发现新的复杂网络模式的潜力是巨大的，最近在这一领域的发展导致了其计算效率的提高。这一建议也是在麦吉尔大学建立一个新的研究所--卢德默神经信息学和心理健康中心的背景下提出的。新的Ludmer中心的研究人员参与了一个特殊的多学科研究平台，目的是了解不同的遗传，表观遗传和环境因素如何影响儿童的大脑发育。该方法结合了神经科学、计算生物学、数学、遗传学、表观遗传学、生物信息学、流行病学和计算机科学等学科。在这方面，拟议的项目非常相关，因此将加强直接的知识转让。特别是，我希望这些发现将为诊断、预防和治疗开辟新的途径。**