CRII: III: Computational Methods to Explore Big Bioassay Data for Better Compound Prioritization

CRII：III：探索大生物测定数据以更好地确定化合物优先级的计算方法

• 批准号: 1855501
• 负责人: Xia Ning
• 金额: $ 10.79万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855501&HistoricalAwards=false
• 关键词: CRII; III; Computational; Methods; Explore

Bioassay data represent an extremely valuable source of experimental Big Data with rich content that have been substantially produced in the early stages of drug discovery for testing chemical compound bioactivities and identifying promising drug candidates. However, the power of such Big bioassay data has not been fully unleashed, particularly for the purposes of discovering novel knowledge and improving drug development. This is largely due to the fact that the exploration of a much larger space of bioassays has been fundamentally hindered by the less developed ability to identify and utilize the relations across bioassays. In this project, the PI and her team will develop novel computational methods and tools that can effectively explore a wide range of heterogeneous bioassays, identify experimentally unrevealed relations among them, and utilize the novel knowledge derived from them so as to improve compound prioritization. The research will bring scientific impacts and shed light on fully utilizing the existing wealth of Big Data, stimulating knowledge distillation in innovative manners, establishing visionary conceptual hypotheses and developing novel analytical techniques correspondingly. This research aims to solve critical problems in drug discovery through Big Data means, and has a great potential to improve drug candidate identification through accurate compound prioritization, and thus it will have far-reaching economic and societal impacts. The PI and her team will develop a computational framework to produce better compound ranking for each bioassay. This framework will consist of a local structure learning component and a global structure learning component to discover and leverage the compound ranking within a bioassay and ranking relations across bioassays, respectively. They will also develop new methods to better rank compounds under a combination of criteria. In particular, they will solve compound ranking based on activity and selectivity simultaneously by leveraging ranking difference across bioassays. The research will be innovative, both in terms of employing original computational models and methods into important problems in drug discovery, and in terms of developing unique methodologies and computational techniques for core Computer Science research. For drug discovery, the research will provide novel perspectives and methodologies as to how researchers can utilize the large-scale experimental data to solve important problems in drug discovery. For core Computer Science, the research will contribute a new solution framework and methods spanning the areas of data mining and machine learning. Specifically, the research will lead to novel methods for boosting ranking performance by actively including additional data, incorporating relevant information within a regularized optimization framework, deploying iterative procedures and greedy strategies for large-scale problems with multiple simultaneous tasks, etc. All these methods are generalizable to a variety of other Computer Science applications. For further information see the project web page: http://cs.iupui.edu/~xning/compRank.html

生物测定数据代表了具有丰富含量的实验大数据的极为宝贵的来源，这些数据在药物发现的早期阶段已经产生，用于测试化学复合生物活性和鉴定有希望的药物候选者。但是，这种大型生物测定数据的力量尚未完全释放，尤其是为了发现新知识并改善药物开发。这在很大程度上是由于以下事实：探索更大的生物测定空间已受到识别和利用整个生物测定之间关系的较少发达能力的阻碍。在这个项目中，PI和她的团队将开发新颖的计算方法和工具，这些方法和工具可以有效地探索广泛的异质生物测定，确定它们之间的实验未透露关系，并利用从中获得的新知识来改善复合优先级。这项研究将带来科学的影响，并阐明充分利用现有的大数据财富，刺激创新方式的知识蒸馏，建立有远见的概念假设并相应地开发新颖的分析技术。这项研究旨在通过大数据手段来解决药物发现中的关键问题，并具有通过准确的复合优先级改善药物识别的巨大潜力，因此它将产生深远的经济和社会影响。 PI和她的团队将开发一个计算框架，为每个生物测定法提供更好的复合排名。该框架将包括本地结构学习组件和一个全球结构学习组件，以分别在生物测定中发现和利用化合物排名和整个生物测定的排名。他们还将开发新的方法，以在标准组合结合下更好地排名化合物。特别是，他们将通过利用整个生物测定的排名差异来基于活动和选择性来解决复合排名。这项研究将是创新的，无论是将原始的计算模型和方法用于药物发现中的重要问题，以及为核心计算机科学研究开发独特的方法和计算技术而言。对于药物发现，该研究将提供有关研究人员如何利用大规模实验数据来解决药物发现中重要问题的新观点和方法。对于核心计算机科学，该研究将为涵盖数据挖掘和机器学习领域的新解决方案框架和方法。具体而言，这项研究将通过主动包括其他数据，将相关信息纳入正规化优化框架，部署迭代程序和贪婪的策略，以解决具有多个同时任务的大规模问题等。所有这些方法都是其他各种计算机科学应用程序都可以推广的大规模问题。有关更多信息，请参见项目网页：http：//cs.iupui.edu/~xning/comprank.html