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

现代技术已经完全改变了生物和信息科学中数据的概念。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节和代谢动力学的数据收集是极其庞大和异构的。这些集合通常以网站或蛋白质网络为特征，其中有向边表示信息流或化学反应，并以网页或氨基酸链描述节点信息。知识发现和管理是关键。本提案的目标是创建新的计算和统计方法来有效地存储、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有助于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、数据分析和传播方面。该方法是开发新的模型、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；比较不同网络模型的拟合性；有效地存储网络中连接和节点的信息；校准网络的信息先验，这些网络反映了代谢网络和网络上新闻传播中的信号现实，用于经验贝叶斯分析；估计节点信息对网络局部连通性的影响；并推断在线信息网络的影响潜力和传播渠道。本文的研究主要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元网络的新表示；(2)开发一种灵活的概率图形模型，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，从网络本身的信息工件的多条轨迹推断出不可观察的网络结构。此外，将进行两个深入的个案研究，以说明拟议方法的潜力。首先分析了在线报纸、新闻采集者和博客之间的地方影响模式对新闻和信息项目传播的影响。第二部分分析了调控网络中信号局部扰动对从细菌到人类的许多已知功能的整体细胞反应的影响。在处理案例研究中获得的见解将反过来推广和促进机器学习中下一波核心方法和理论的发展。建议的工作满足了开发新的和有原则的方法来分析大量网络数据的迫切需要，以及创建用于测试和基准测试的大规模数据集，以使整个社区受益。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，这将吸引许多本科生在机器学习和科学的交叉领域进行研究，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，使社区能够使用和构建这些工具。

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.