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

点击翻译按钮获取中文摘要

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.