CAREER: Mining Genome-wide Chemical-Structure Activity Relationships in Emergent Chemical Genomics Databases

职业：在新兴化学基因组数据库中挖掘全基因组化学结构活性关系

• 批准号: 0845951
• 负责人: Jun Huan
• 金额: $ 50万
• 依托单位: University of Kansas Center for Research Inc
• 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: 2009-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845951&HistoricalAwards=false
• 关键词: CAREER; Mining; Genome; wide; Chemical

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this proposal is to develop an integrated research and education program for advancing the underlying theoretical and computational principles of data mining in the emergent chemical genomics databases. The core technical innovations that this research aims to advance are: (i) developing effective kernel-based representations and structure pattern extraction and selection methods to capture the intrinsic characteristics of irregular and discrete spaces such as the chemical space, (ii) designing methods for adaptive and scalable similarity search in large databases of complex data and methods for accurate classification models, and (iii) deriving application oriented validation. A key strength of this work is the application of the theoretic and computational advancements to real-world problems, namely, chemical toxicity prediction based on microarray gene expression profiles and high-throughput chemical screening. Collaborators in academia, industry, and government agencies will evaluate the new algorithms. The data mining knowledge gained will be applicable beyond the chemical domain; examples of such applications include social network analysis and sensor network analysis. The PI will work closely with the Center of Excellence in Chemical Methodologies and Library Development at the University of Kansas (KU CMLD) to evaluate research prototypes. Intellectual MeritThis research addresses the fundamental problem of learning functional dependencies between arbitrary input and output domains. In particular, this research: 1) focuses on complex input domain, the space of all chemicals, 2)aims to derive a uniform representation of the domain by working on innovative tools for graphs and geometric structures that are associated with the domain, 3) will provide practical tools to search through the domain, and will design new algorithms that uncover real connections between the input domain to an equally complex output domain (a space of biological entities). The data mining knowledge gained from this project will provide the research community with much better techniques for searching, mining, and analyzing domains of complex data and for uncovering the real connections between domains of complex data. The proposed research is a timely effort to integrate and advance knowledge in three communities: cheminformatics, data mining, and machine learning. Broader ImpactAccurate data mining tools for chemical structure-activity relationship discovery will simplify and accelerate drug discovery and hence improve human health. Better prediction tools for chemical activity including toxicity will lead to better strategies for environmental monitoring and preservation. Deep understanding of chemical structure-activity relationships should enable rational material design in the research for renewable and clean energy. The research program is strongly linked to the educational goals of this proposal, which are, among others, (i) to enrich curriculum for the undergraduate and graduate education in new interdisciplinary training programs and (2) to encourage K-12 and undergraduate students to pursue careers in Science, Technology, Engineering, and Mathematics (STEM).

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该提案的目标是开发一个综合的研究和教育计划，以推进新兴化学基因组数据库中数据挖掘的基本理论和计算原理。本研究旨在推进的核心技术创新是：（i）开发有效的基于核的表示和结构模式提取和选择方法，以捕获诸如化学空间的不规则和离散空间的内在特征，（ii）设计用于在复杂数据的大型数据库中进行自适应和可缩放的相似性搜索的方法和用于精确分类模型的方法，以及（iii）导出面向应用的验证。这项工作的一个关键优势是将理论和计算的进步应用于现实世界的问题，即基于微阵列基因表达谱和高通量化学筛选的化学毒性预测。学术界、工业界和政府机构的合作者将对新算法进行评估。所获得的数据挖掘知识将适用于化学领域以外的领域;这些应用的例子包括社会网络分析和传感器网络分析。PI将与堪萨斯大学（KU CMLD）的化学方法学和图书馆开发卓越中心密切合作，以评估研究原型。智力MeritThis研究解决了学习任意输入和输出域之间的函数依赖关系的基本问题。特别是，这项研究：1）关注复杂的输入域，所有化学品的空间，2）旨在通过致力于与域相关联的图形和几何结构的创新工具来获得域的统一表示，3）将提供实用的工具来搜索域，并将设计新的算法来揭示输入域与同样复杂的输出域（生物实体的空间）之间的真实的联系。从这个项目中获得的数据挖掘知识将为研究界提供更好的技术来搜索、挖掘和分析复杂数据的域，并揭示复杂数据域之间的真实的联系。拟议的研究是在三个社区整合和推进知识的及时努力：化学信息学，数据挖掘和机器学习。更广泛的影响用于化学结构-活性关系发现的准确数据挖掘工具将简化和加速药物发现，从而改善人类健康。更好的化学活动预测工具，包括毒性，将导致更好的战略，环境监测和保护。深入了解化学结构与活性的关系，可以在可再生能源和清洁能源的研究中进行合理的材料设计。该研究计划与本提案的教育目标密切相关，其中包括：（i）丰富新的跨学科培训计划中的本科生和研究生教育课程;（2）鼓励K-12和本科生追求科学，技术，工程和数学（STEM）。