III: Medium: Collaborative Research: Integration, Prediction, and Generation of Mixed Mode Information using Graphical Models, with Applications to Protein-Protein Interactions

III：媒介：协作研究：使用图形模型整合、预测和生成混合模式信息，并应用于蛋白质-蛋白质相互作用

• 批准号: 0905206
• 负责人: Christopher Bailey-Kellogg
• 金额: $ 28.88万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905206&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Integration

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Probabilistic graphical models provide a powerful mechanism for representing and reasoning with uncertain information. These methods have been successfully applied in diverse domains such as bioinformatics, social networks, sensor networks, robotics, and web mining; in turn, such application areas have posed new computational challenges driving graphical model research. This project is motivated by challenges in emerging application areas such as epidemiological simulation, geoscience modeling, and studies of interacting proteins, where there are rich sets of information of multiple types and at multiple levels of granularity. While the methods developed will be general, the research will focus on protein-protein interactions, which drive the molecular machinery of the cell by forming transient or persistent complexes to propagate signals, catalyze reactions, transport molecules, and so forth. The mixed-mode information available includes amino acid sequences, three-dimensional structures and associated physical models, and binary, rank-ordered, or even quantitative interaction data. The proposed techniques address key challenges in information integration, prediction, and generation using graphical models. Intellectual merits: The intellectual merits of this work derive both from the new capabilities for information integration and for reasoning with probabilistic graphical models, as well as their application to the study of protein-protein interactions. Proteins offer, by far, some of the most complex, multi-faceted datasets for integration using computational methods; hence the lessons learned here can be applied to similarly rich information spaces, such as epidemiology and geosciences. These integrated models of interacting proteins and new algorithms for prediction and generation will also support significant applications such as protein engineering and systems biology, bridging interaction networks to the underlying residue-level interactions in order to better understand and control them. Broader impacts: This project will reach out to both the bioinformatics and larger computer science communities to maximize the impact of our contributions. An open-source integrator platform will be developed, aimed at integrating protein datasets and which can be extended to information integration in other domains as well. To stimulate community building and foster discovery, the research team will advocate situating computer science research in the context of concrete applications. Building on prior successes, the team will organize a workshop at a suitable venue such as ICML/AAAI/NIPS/KDD focused on an 'information integration challenge' dataset involving protein modeling. Finally, through programs such as Women@SCS at Carnegie Mellon, WISP (Women in Science Program) at Dartmouth, Howard Hughes education grant internships at Purdue, and the MAOP/VTURCS (Minority Academic Opportunities Program and VT Undergraduate Research in Computer Science) program at Virginia Tech, the team will provide cross-disciplinary training to undergraduate students from underrepresented groups. Keywords: Probabilistic Graphical Models, Information Integration, Mixed-Mode Datasets, Bioinformatics, Proteins, Markov Chain Monte Carlo (MCMC) methods.

该奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。概率图形模型提供了一种强大的机制，用于用不确定的信息来表示和推理。这些方法已成功应用于生物信息学，社交网络，传感器网络，机器人技术和网络挖掘等不同领域。反过来，此类应用领域提出了推动图形模型研究的新计算挑战。该项目是由新兴应用领域（例如流行病学模拟，地球科学建模和相互作用蛋白质的研究）所挑战的挑战，那里有多种类型的信息集和多种水平的粒度信息。虽然开发的方法将是一般的，但该研究将集中在蛋白质蛋白相互作用上，这些相互作用通过形成瞬态或持续的复合物来传播信号，催化反应，转运分子等，从而驱动细胞的分子机械。可用的混合模式信息包括氨基酸序列，三维结构和相关的物理模型，以及二进制，等级排序甚至定量相互作用数据。提出的技术使用图形模型解决了信息整合，预测和生成中的关键挑战。智力优点：这项工作的智力优点既来自信息整合的新功能，也取决于概率图形模型的推理，以及它们在研究蛋白质蛋白质相互作用的研究中的应用。到目前为止，蛋白质提供了一些最复杂，多面的数据集，用于使用计算方法进行集成；因此，这里学到的经验教训可以应用于类似的丰富信息空间，例如流行病学和地球科学。这些相互作用蛋白质的集成模型和用于预测和生成的新算法还将支持重要的应用，例如蛋白质工程和系统生物学，将相互作用网络桥接到基础残基级相互作用，以更好地理解和控制它们。更广泛的影响：该项目将涉及生物信息学和更大的计算机科学社区，以最大程度地发挥我们的贡献影响。将开发一个开源集成仪平台，旨在集成蛋白质数据集，并可以将其扩展到其他域中的信息集成。为了刺激社区建设和促进发现，研究团队将在具体应用程序的背景下倡导计算机科学研究。在先前成功的基础上，团队将在合适的场地（例如ICML/AAAI/NIPS/KDD）组织一个研讨会，该研讨会着重于涉及蛋白质建模的“信息集成挑战”数据集。最后，通过在卡内基·梅隆（Carnegie Mellon）的女性@scs，达特茅斯（Dartmouth）的WISP（女性科学计划），霍华德·休斯（Howard Hughes）教育授予普渡大学的实习，以及MAOP/VTURCS（MAOP/VTURCS（少数族裔学术机会计划和VT弗吉尼亚大学计算机科学领域的VT本科研究），团队将为跨学科的培训提供跨学科的培训，以提供跨学科培训，以提供跨学科的学生，培训不足的学生。关键字：概率图形模型，信息集成，混合模式数据集，生物信息素，蛋白质，马尔可夫链蒙特卡洛（MCMC）方法。