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

生物测定数据是实验大数据的一个极其宝贵的来源，这些数据具有丰富的内容，在药物发现的早期阶段大量产生，用于测试化合物的生物活性和确定有前途的候选药物。然而，这种大生物化验数据的力量还没有完全释放出来，特别是在发现新知识和改进药物开发方面。这在很大程度上是因为，由于识别和利用生物检测之间的关系的能力不发达，从根本上阻碍了对更大生物检测空间的探索。在这个项目中，PI和她的团队将开发新的计算方法和工具，这些方法和工具可以有效地探索广泛的异质生物分析，识别它们之间的实验未揭示的关系，并利用从它们获得的新知识，以提高化合物的优先顺序。这项研究将带来科学影响，并有助于充分利用现有的大数据财富，以创新的方式刺激知识蒸馏，建立富有远见的概念假设，并相应开发新的分析技术。这项研究旨在通过大数据手段解决药物发现中的关键问题，并通过准确的化合物优先顺序来提高药物候选识别的潜力，因此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，为每个生物测试产生更好的化合物排名。这一框架将由局部结构学习部分和全局结构学习部分组成，以分别发现和利用生物测定中的化合物排名和生物测定之间的排名关系。他们还将开发新的方法，在组合标准下更好地对化合物进行排名。特别是，他们将利用生物检测中的排名差异，同时解决基于活性和选择性的化合物排名问题。这项研究将是创新的，无论是在将原始计算模型和方法应用于药物发现的重要问题方面，还是在为核心计算机科学研究开发独特的方法和计算技术方面。对于药物发现，这项研究将为研究人员如何利用大规模实验数据来解决药物发现中的重要问题提供新的视角和方法。对于核心计算机科学，这项研究将贡献一个新的解决方案框架和方法，跨越数据挖掘和机器学习领域。具体地说，该研究将通过主动包含额外数据、将相关信息纳入正则化优化框架、为具有多个同时任务的大规模问题部署迭代程序和贪婪策略等方法来提高排序性能。所有这些方法都可以推广到其他各种计算机科学应用程序。有关更多信息，请参阅项目网页：http://cs.iupui.edu/~xning/compRank.html

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