CAREER: Computational strategies for incompleteness and heterogeneity in multi-omic data

职业：多组学数据不完整性和异质性的计算策略

• 批准号: 1942394
• 负责人: Jingwen Yan
• 金额: $ 54.99万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942394&HistoricalAwards=false
• 关键词: CAREER; Computational; strategies; incompleteness; heterogeneity

Multi-omics refers to the integrative analysis of multiple types of -omics data (e.g., genotype, gene expression and protein expression). Increasing multi-omic data provides opportunities for discovery of disease biomarkers from multiple molecular scales and therefore can further our understanding of underlying disease mechanisms. Despite this great potential, existing multi-omic data collections are mostly incomplete and of heterogeneous types (e.g., continuous and categorical numbers). Integrating these data for joint analysis typically requires exclusion of many subjects with missing values; as a consequence, a large chunk of data remains unused. This project provides novel perspectives in handling the incompleteness and heterogeneity problems in multi-omics data and hereafter allow biomedical researchers to gain more insights from rapidly growing yet imperfect biomedical data. In addition, the increasing multi-omics data has led to a massive transformation in biomedical research and has resulted in an unprecedented need in information management, decision support, and advanced analytics. In this project, a series of educational activities will be conducted to engage students at their early stages of education and to increase their awareness of educational opportunities and career paths in biomedical informatics.This project aims to develop new classes of computational methods to enable the joint mining of incomplete and heterogeneous multi-omic data by leveraging various biological networks for discovery of functionally connected biomarkers. Towards this, two tasks will be performed: 1) identify multi-omic subnetworks as biomarkers via a multi-task joint network module detection and feature selection model, and 2) select associated features between heterogeneous -omics layers via a novel multi-task sparse association model. The first task aims to address the incomplete data problem. This new model can not only handle the incomplete data collected from one large-scale project, but also allow the joint analysis of -omics data from multiple small-scale projects without overlap in subjects. The second task addresses the heterogeneity problem with a novel two-step strategy in associating different -omics layers. Built upon these research efforts, three outreach educational activities will be conducted: 1) develop a project-based curriculum for high school students, 2) host an annual summer workshop on multi-omics for high school students, and 3) provide advanced research opportunities to undergraduates from biomedical informatics and related disciplines. This research effort will lead to discovery of more reliable biomarkers for further validation and better understanding of their relationships with disease traits than currently possible.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.

多组学是指对多种类型的组学数据（如基因型、基因表达和蛋白质表达）进行综合分析。越来越多的多组学数据为从多个分子尺度发现疾病生物标志物提供了机会，因此可以进一步了解潜在的疾病机制。尽管有巨大的潜力，但现有的多组学数据收集大多是不完整和异构类型的（例如，连续数和分类数）。整合这些数据进行联合分析通常需要排除许多缺失值的主题；因此，大量数据仍然未被使用。该项目为处理多组学数据的不完整性和异质性问题提供了新的视角，使生物医学研究人员能够从快速增长但不完善的生物医学数据中获得更多的见解。此外，越来越多的多组学数据导致了生物医学研究的巨大转变，并导致了对信息管理、决策支持和高级分析的前所未有的需求。在这个项目中，将开展一系列的教育活动，让学生在教育的早期阶段参与进来，提高他们对生物医学信息学的教育机会和职业道路的认识。该项目旨在开发新的计算方法，通过利用各种生物网络来发现功能连接的生物标志物，从而实现对不完整和异构多组学数据的联合挖掘。为此，将完成两个任务：1)通过多任务联合网络模块检测和特征选择模型识别多组子网络作为生物标志物；2)通过新的多任务稀疏关联模型选择异构组学层之间的相关特征。第一个任务旨在解决数据不完整的问题。该模型不仅可以处理从一个大型项目中收集的不完整数据，还可以对多个小型项目中不重叠的组学数据进行联合分析。第二个任务是通过一种新的两步策略来解决异质性问题，将不同的组学层关联起来。在这些研究成果的基础上，将开展三项外展教育活动：1)为高中生开发基于项目的课程；2)为高中生举办一年一度的暑期多组学研讨会；3)为生物医学信息学和相关学科的本科生提供先进的研究机会。这项研究工作将导致发现更可靠的生物标志物，以进一步验证和更好地了解它们与疾病特征的关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。