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

生物测定数据是一个非常有价值的实验大数据来源，内容丰富，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希望的候选药物。然而，这种大型生物测定数据的力量尚未得到充分释放，特别是在发现新知识和改进药物开发方面。这在很大程度上是由于对更大的生物测定空间的探索从根本上受到了识别和利用生物测定之间关系的欠发达能力的阻碍。在这个项目中，PI和她的团队将开发新的计算方法和工具，可以有效地探索广泛的异质生物测定，识别实验中未揭示的关系，并利用从中获得的新知识来提高化合物的优先级。该研究将带来科学影响，并有助于充分利用大数据的现有财富，以创新的方式激发知识升华，建立有远见的概念假设，并相应地开发新的分析技术。本研究旨在通过大数据手段解决药物发现中的关键问题，具有通过精确的化合物优先排序来提高候选药物鉴定的巨大潜力，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，为每种生物测定方法提供更好的化合物排名。该框架将由局部结构学习组件和全局结构学习组件组成，分别用于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，根据一系列标准对化合物进行更好的排序。特别是，他们将通过利用生物测定法之间的排名差异，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，无论是在使用原始的计算模型和方法来解决药物发现中的重要问题方面，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，该研究将为研究人员如何利用大规模实验数据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，该研究将提供一个跨越数据挖掘和机器学习领域的新的解决方案框架和方法。具体而言，该研究将通过积极地包括额外数据、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过程和贪婪策略等方法来提高排名性能。所有这些方法都可以推广到其他各种计算机科学应用中。欲了解更多信息，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