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

III：小型：大型生物和信息网络的表示、建模和推理

• 批准号: 1017967
• 负责人: Edoardo Airoldi
• 金额: $ 49.78万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017967&HistoricalAwards=false
• 关键词: III; Small; Representation; Modeling; Inference

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.

现代技术已经完全改变了生物和信息科学中的数据概念。例如，关于网络上信息流的数据收集，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非常庞大和异构的。这些集合通常被表征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，并且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型计算和统计方法，以有效地存储、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帮助解决许多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、传播等方面，途径是开发新的模型、方法和算法，用于分析节点信息丰富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拟合度;有效地存储关于网络中的连通性和节点的信息;校准反映代谢网络和网络上新闻传播中信号传递的现实的网络的信息先验，用于经验贝叶斯分析;估计节点信息对网络中局部连通性的影响;并推断在线信息网络中的影响潜力和扩散渠道。拟议的研究重点是三个具体的技术任务：（1）基于统计模型建立有值多元网络的新表示;（2）开发灵活的族或概率图模型，将网络中的本地连接性链接到高维节点属性;以及（3）开发可扩展算法以从网络本身上的信息人工产物的多个踪迹推断不可观察的网络结构。此外，还将开展两项深入的案例研究，以说明拟议方法的潜力。第一个是分析在线报纸、新闻收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。第二个是对从细菌到人类的许多已知功能的调节网络中的信号传导的局部扰动对全局细胞反应的影响的分析。在解决案例研究中获得的见解将反过来推广和促进机器学习下一波核心方法和理论的发展。拟议的工作满足了迫切需要开发新的和原则性的方法来分析大量的网络数据，以及创建用于测试和基准测试的大规模数据集，以造福整个社区。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，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的群体的学生从事计算机科学和统计学的职业。该小组将分发开放源码软件，并建立网站，使社区能够使用和建立这些工具。