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《美国复苏和再投资法案》(公法111-5)提供资金的。概率图模型为不确定信息的表示和推理提供了一种强有力的机制。这些方法已经成功地应用于生物信息学、社会网络、传感器网络、机器人和网络挖掘等领域，反过来，这些应用领域也给图形模型研究带来了新的计算挑战。这个项目的动机是新兴应用领域的挑战，如流行病学模拟、地学建模和相互作用蛋白质的研究，这些领域有多种类型和多个粒度的丰富信息集。虽然开发的方法将是通用的，但研究重点将放在蛋白质-蛋白质相互作用上，这种相互作用通过形成瞬时或持久的复合体来驱动细胞的分子结构，以传播信号、催化反应、运输分子等。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、排序甚至定量的相互作用数据。提出的技术解决了信息集成、预测和使用图形模型生成的关键挑战。智力优势：这项工作的智力优势来自于信息集成和概率图形模型推理的新能力，以及它们在蛋白质-蛋白质相互作用研究中的应用。到目前为止，蛋白质提供了一些最复杂的、多方面的数据集，用于使用计算方法进行整合；因此，这里学到的经验教训可以应用于同样丰富的信息空间，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新的预测和生成算法也将支持蛋白质工程和系统生物学等重要应用，将相互作用网络与潜在的残基水平相互作用联系起来，以便更好地理解和控制它们。更广泛的影响：这个项目将接触到生物信息学和更大的计算机科学社区，以最大限度地发挥我们贡献的影响。将开发一个开源的集成器平台，旨在集成蛋白质数据集，该平台也可以扩展到其他领域的信息集成。为了促进社区建设和促进发现，研究小组将倡导将计算机科学研究置于具体应用的背景下。在以往成功的基础上，该小组将在ICML/AAAI/NIPS/KDD等合适的地点组织一次研讨会，重点讨论涉及蛋白质建模的“信息整合挑战”数据集。最后，通过卡内基梅隆大学的女性@SCS计划、达特茅斯大学的WISP(女性参与科学计划)、普渡大学的霍华德·休斯教育助学金实习以及弗吉尼亚理工大学的MAOP/VTURCS(少数族裔学术机会计划和VT本科生计算机科学研究)计划，该团队将为来自代表性不足群体的本科生提供跨学科培训。关键词：概率图形模型、信息集成、混合模式数据集、生物信息学、蛋白质、马尔可夫链蒙特卡罗(MCMC)方法。