III: Small: Representation, Modeling and Inference for Large Biological and Information Networks

现代技术彻底改变了生物和信息科学中的数据概念。例如，关于网络信息流的数据收集，或者关于驱动细胞功能的调控和新陈代谢动力学的数据收集，都是极其庞大和不同种类的。这些集合通常被描述为网站或蛋白质的网络，其中有向边表示信息流或化学反应，节点信息以网页或氨基酸链的形式描述。知识发现和管理是关键。该提案的目标是创建新的计算和统计方法来高效地存储、搜索和量化大型网络中的模式，并探索这些新工具在多大程度上帮助解决一些重要的开放问题和计算问题。研究计划包括理论、方法、数据分析和传播等方面，其途径是开发新的模型、方法和算法来分析具有丰富节点信息的大型生物和信息网络。将开发新的工具：评估网络的复杂性；比较备选网络模型的适用性；有效存储关于网络中连通性和节点的信息；校准反映新陈代谢网络和网上新闻传播实际情况的网络的信息先验，以便进行经验贝叶斯分析；估计节点信息对网络本地连通性的影响；以及推断在线信息网络中的影响潜力和传播渠道。提出的研究集中在三个具体的技术任务上：(1)基于统计模型建立有价值的多变量网络的新表示；(2)开发灵活的家族或概率图形模型，将网络中的局部连通性与高维节点属性联系起来；(3)开发可扩展的算法，从网络本身的多个信息伪迹推断不可观测的网络结构。此外，还将开发两个深入的案例研究，以说明拟议方法的潜力。首先分析了网络报纸、新闻采集者和博客之间的地方影响力模式对新闻和信息项传播的影响。第二个是分析调控网络中信号的局部扰动对从细菌到人类的许多已知功能的全球细胞反应的影响。从处理案例研究中获得的见解将反过来概括和促进机器学习中下一波核心方法和理论的发展。拟议的工作满足了开发新的原则性方法来分析海量网络数据的迫切需要，以及创建用于测试和基准的大规模数据集，以造福于整个社区。该研究计划与跨学科教育计划和统计机器学习课程的开发紧密结合在一起，这将吸引许多本科生在机器学习和科学的交叉点进行研究，并将提供机会，积极鼓励来自代表性不足群体的学生从事计算机科学和统计方面的职业。该团队将分发开源软件并建立网站，以使社区能够使用这些工具并在此基础上进行构建。

Modern technology has completely transformed the concept of data in the biological and information sciences. Data collections about the flow of information on the web, for instance, or about regulatory and metabolic dynamics that drive cellular functionality are extremely large and heterogeneous. These collections are often characterized as networks of websites, or proteins, where directed edges denote information flow, or chemical reactions, and with node information described in terms of web pages, or chains of amino acids. Knowledge discovery and management is key. The goal of this proposal is to create novel computational and statistical approaches to store, search, and quantify patterns in large networks efficiently, and to explore the extent to which these new tools help address a number of important open problems and computational issues. The research plan includes theoretical, methodological, data analysis, and dissemination aspects.The approach is to develop new models, methods and algorithms for analyzing large biological and information networks with rich node information. New tools will be developed: to assess the complexity of networks; to compare the fit of alternative network models; to store information about both connectivity and nodes in a network efficiently; to calibrate informative priors for networks that reflect the reality of signaling both in metabolic networks and in the spread of news on the web for empirical Bayesian analyses; to estimate the effects of node information on the local connectivity in a network; and to infer influence potentials and diffusion channels in online information networks. The proposed research is focused on three specific technical tasks: (1) establishing a new representation of valued, multivariate networks based on a statistical models; (2) developing a flexible family or probabilistic graphical models to link local connectivity in the network to high-dimensional node attributes; and (3) developing scalable algorithms to infer a non-observable network structure from multiple trails of informational artifacts on the network itself. In addition, two in-depth case studies will be developed to illustrate the potential of the proposed methodology. The first is an analysis of the effects of local influence patterns among online newspapers, news collectors and blogs on the diffusion of news and information items. The second is an analysis of the effects of local perturbations of signaling in regulatory networks on global cellular responses, for many known functions, from bacteria to human. Insights gained in tackling the case studies will in turn generalize and foster the development of the next wave of core methodology and theory in machine learning.The proposed work meets an urgent need for the development of new and principled methods for analyzing massive amounts of network data, as well as the creation of large-scale data sets for testing and benchmarking, to the benefit of the community at large. The research plan is tightly integrated with an interdisciplinary educational program and with the development of a statistical machine learning curriculum, which will attract many undergraduates to research at the intersection of machine learning and the sciences, and will provide opportunities to actively encourage students from underrepresented groups to pursue careers in computer science and statistics. The team will distribute open source software and set-up websites to enable the community to use and build upon the tools.