CRII:SCH:Computational Methods to Mine Multi-omic Data for Systems Biology of Complex Diseases

CRII：SCH：挖掘复杂疾病系统生物学多组学数据的计算方法

• 批准号: 1755836
• 负责人: Jingwen Yan
• 金额: $ 17.48万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755836&HistoricalAwards=false
• 关键词: CRII; SCH; Computational; Methods; Mine

Recent advances in high throughput technologies have led to a substantial increase in multi-omic data characterizing various levels of molecular changes in the progression of disease, including genome, transcriptome, proteome and metabolome. The availability of computational methods that are sufficiently powerful to handle the high dimensionality and heterogeneity of multi-omic data is still very limited. In addition, major findings generated from current -omics studies have been largely restricted to relatively simple patterns, e.g., individual biomarkers, possibly with few functional interactions, which present difficulties for validating these findings and relating them to downstream biology. This project, by coupling the multi-omic data and the systems biology networks, will develop novel computational methods to explore the functional network modules associated with disease quantitative traits. By enabling both strategic and efficient knowledge extraction from the vast biological landscape represented by multi-omic data, this research has may lead to unprecedented discovery of disease mechanisms and suggest surrogate biomarkers for therapeutic trials.This work will develop new computational methods to enable the integration of large scale heterogeneous multi-omic data with rich domain knowledge for better biomarker and association discovery. Two interrelated tasks will be performed: 1) Develop a novel biological knowledge guided structured sparse learning model together with large-scale optimization methods to integrate -omic data and biological networks from multiple sources and discover -omic modules involving heterogeneous biomarkers for accurately predicting outcomes of interest; and 2) Couple multi-task learning with structured sparse association models to jointly learn the bi-multivariate associations between imaging phenotypes and -omic features with dense functional connections for multiple groups. The project will contribute to a new solution framework spanning the areas of machine learning, data mining and network science, and also provide novel perspectives as to how to effectively integrate the large-scale and heterogeneous -omic data for a systems biology of complex diseases.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.

高通量技术的最新进展导致表征疾病进展中各种水平分子变化的多组学数据大幅增加，包括基因组、转录组、蛋白质组和代谢组。 足够强大的计算方法来处理多维和异质性的多组学数据的可用性仍然非常有限。此外，从当前组学研究中产生的主要发现在很大程度上限于相对简单的模式，例如，单个生物标志物，可能具有很少的功能相互作用，这为验证这些发现并将其与下游生物学相关联带来了困难。本计画将多组学资料与系统生物学网路结合，发展新的计算方法，以探索与疾病数量性状相关的功能网路模组。通过从多组学数据所代表的巨大生物景观中进行战略性和有效的知识提取，这项研究可能会导致前所未有的疾病机制发现，并为治疗试验提供替代生物标志物。这项工作将开发新的计算方法，使大规模异质多组学数据与丰富的领域知识相结合，以更好地发现生物标志物和关联。将执行两个相互关联的任务：1）开发新的生物知识引导的结构化稀疏学习模型以及大规模优化方法，以整合来自多个来源的组学数据和生物网络，并发现涉及异质生物标志物的组学模块，用于准确预测感兴趣的结果;以及2）将多任务学习与结构化稀疏关联模型耦合，以共同学习成像表型和-具有多个组的密集功能连接的组学特征。该项目将有助于建立一个新的解决方案框架，涵盖机器学习、数据挖掘和网络科学等领域，并提供了新的视角，如何有效地整合大规模和异构-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

项目成果

Disruption of gene co-expression network along the progression of Alzheimer's disease

阿尔茨海默病进展过程中基因共表达网络的破坏

• DOI: 10.1109/bhi.2019.8834551
• 发表时间: 2019
• 期刊: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI
• 作者: Upadhyaya, Yurika;Xie, Linhui;Salama, Paul;Nho, Kwangsik;Saykin, Andrew J.;Yan, Jingwen
• 通讯作者: Yan, Jingwen