Mining Multi-Layer Protein-Protein Association Networks: An Integrated Spectral Approach

这个项目的重点是帮助我们获得生物系统中相关的蛋白质或基因对的信息的策略。除了收集关于不同基因或蛋白质在细胞中的作用的大量信息外，还收集了越来越多关于相关蛋白质或基因对的信息，要么是因为有证据表明它们在细胞中合作，要么是因为有证据表明它们在其他方面具有共同的属性。关于关联对的信息可以在数学上描述为一个异质的网络集合，但设计高效的机器学习和计算数学算法来集成不同的信息源来探索和理解这些网络是一个困难的悬而未决的问题。这个项目中将开发的新的数学方法将为计算生物学家和系统生物学家定制，他们希望使用网络分析来提高他们数据中重要基因和通路的信号的统计意义，并将其应用于基因功能预测，以及识别在II型糖尿病和克罗恩病等复杂疾病中重要的基因集。该项目资助一名研究生和两名本科生。通过与研究人员和该领域其他专家的培训和合作，他们将参与到更广泛的科学计算和生物学研究社区中。将通过集成的谱方法和基于扩散的距离度量的推广，开发、分析和实现有效和高效的推理和计算方法，用于挖掘多层PPI网络。更准确地说，将设计基于降维和多层次优化方法的谱多层分析方法，以提供高质量的多个网络的集成工具，可以用于挖掘这些海量的图集。这些方法将在一个实质性的新生物网络试验台上进行基准测试和测试，与最近的梦幻疾病模块识别挑战相联系。此外，这些工具的实现将普遍提供给社区，用于挖掘一般的不同类型的网络集合，这将导致与计算生物学核心问题相关的新见解，包括识别数据集中的疾病模块。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project is focused on strategies which help us to obtain information for associated pairs of proteins or genes in biological systems. In addition to the collection of lots of information about the role of different genes or proteins in the cell, there is also increasing information about pairs of proteins or genes that are related, either because there is evidence that they cooperate in the cell, or evidence that they otherwise have common attributes. The information about associated pairs can be mathematically described as a heterogeneous collection of networks, but designing efficient and effective machine learning and computational mathematics algorithms to integrate the diverse information sources to explore and make sense of these networks is a difficult unsolved problem. The new mathematical methods that will be developed in this project will be customized for the computational biologists and systems biologists who would like to use network analysis to boost the statistical significance of the signal of important genes and pathways in their data, with applications to gene function prediction, and the identification of sets of genes that are important in complex diseases such as type II diabetes and Crohn's disease. The project supports one graduate student and two undergraduate students. Through training and collaborating with investigators and other experts in the field, they will become involved in the broader research communities of scientific computing and biology.Effective and efficient inference and computational methods will be developed, analyzed, and implemented for mining multi-layer PPI networks via an integrated spectral approach and the generalizations of diffusion-based distance metrics. More precisely, spectral multilayer analysis methods based on dimension reduction and multilevel optimization methods will be designed in order to provide high-quality integration tools of multiple networks that can be used to mine this massive graph collection. The methods will be benchmarked and tested on a substantial new biological network testbed, connected with the recent DREAM disease module identification challenge. Furthermore, implementations of the tools will be made generally available to the community, for mining heterogeneous network collections in general, which will lead to new insights related to core problems in computational biology, including the identification of disease modules within the datasets.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.