EAGER: A Novel Algorithmic Framework for Discovering Subnetworks from Big Biological Data

EAGER：一种从生物大数据中发现子网络的新颖算法框架

• 批准号: 1451316
• 负责人: Dongxiao Zhu
• 金额: $ 17.5万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451316&HistoricalAwards=false
• 关键词: EAGER; Novel; Algorithmic; Framework; Discovering

One of the commonly pursued objectives in big data analytics is to find interesting patterns from data. When the data is big and collected from an ensemble of underlying networks, such as molecular profiling data, inferring molecular subnetworks emerged as a promising solution to knowledge discovery from biological big data. A main barrier impeding the discovery is how to effectively use the massive and heterogeneous information from the data, e.g., how to integrate information from rows and columns of the data matrix to efficiently explore the complex space of possible subnetworks. A recent line of research (by the PI and others) has resulted in new algorithms being introduced to this area. Unfortunately, most of these algorithms are neither specifically designed for nor work well with biological big data. The main goal of this project is to develop tailor-made algorithms and software tools to obtain better discovery of subnetworks from ever-increasing biological big data. The broader significance and importance of this project fall into three main areas. First, the subnet algorithms and software tools developed in this proposal will have broad applicability for many scientific domains wherein subnetwork structures are usually desired; this encompasses disciplines ranging from biological, computational, medical and social sciences. The creation of an efficient and user-friendly software toolbox would further provide rich resources for training and educating students in these scientific domains, thereby helping to ensure national academic competitiveness. Second, the regularly scheduled outreach activities will provide an innovative learning model for educating students of all levels and the community at large. Finally, the under-represented groups, such as female and minority students, will be involved through targeted recruiting and information dissemination.Technically, a novel algorithmic framework, i.e., subnet, will be developed and implemented to discover subnetworks jointly from molecule abundance values and co-regulated molecule sets extracted from the same biological big data. The former correspond to the rows and the latter correspond to the column of the data matrix. Previous research has focused on either columns or rows but not on both simultaneously. A novel multi-criteria score-and-search paradigm will be introduced and a novel subnet algorithm will be developed and implemented to efficiently and reliably extract underlying subnetworks from biological big data. These techniques are transformative in that they are applicable to many other scientific areas where big data are "emitted" by the underlying networks. The algorithms and tools will be systematically evaluated on simulation data sets using standard measures.

大数据分析中通常追求的目标之一是从数据中找到有趣的模式。当数据很大并从基础网络的集合（例如分子分析数据）中收集时，推断出分子子网作为从生物学大数据中发现知识发现的有希望的解决方案。阻碍发现的主要障碍是如何有效地使用数据中的大量和异质信息，例如，如何整合来自数据矩阵的行和列的信息以有效地探索可能的子网的复杂空间。最近的一项研究（PI和其他研究）导致将新算法引入该领域。不幸的是，这些算法中的大多数既不专门为生物大数据设计，也不是很好。该项目的主要目标是开发量身定制的算法和软件工具，以从不断增加的生物学大数据中更好地发现子网。该项目的更广泛的意义和重要性属于三个主要领域。首先，本提案中开发的子网算法和软件工具将对许多科学领域具有广泛的适用性，其中通常需要子网结构；这包括从生物，计算，医学和社会科学的学科。创建高效且用户友好的软件工具箱将进一步为这些科学领域的学生提供丰富的资源，从而帮助确保国家的学术竞争力。其次，定期安排的外展活动将为各个层次和整个社区的学生提供创新的学习模型。最后，将通过有针对性的招聘和信息传播等代表性不足的群体，例如女性和少数族裔学生。技术上，将开发并实施一种新颖的算法框架，即子网，以发现从分子丰富度值和共同调控的分子集中摘录的基因networks共同发现子网。前者对应于行，后者对应于数据矩阵的列。先前的研究已重点放在列或行上，但并非同时介绍两者。将引入一种新颖的多标准评分和搜索范式，并将开发和实施一种新型的子网算法，以从生物大数据中有效，可靠地提取基础。这些技术具有变革性，因为它们适用于许多其他科学领域，这些科学领域被基础网络“散发”。该算法和工具将在模拟数据集上使用标准措施进行系统评估。