Collaborative Research: A General Framework for High Throughput Biological Learning: Theory Development and Applications

协作研究：高通量生物学习的通用框架：理论发展和应用

• 批准号: 0714669
• 负责人: Shaw-Hwa Lo
• 金额: $ 27万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0714669&HistoricalAwards=false
• 关键词: Collaborative; Research; General; Framework; Throughput

This application presents a comprehensive research plan for the investigation of a general framework and various new methods to handle complex large-scale data sets generated from biological (medical) as well as other scientific studies. Two goals are articulated in this proposal: theory development and application in biology and medicine. The former is focused on the study of a general yet core, model-free framework to effectively address major issues arising from high dimensional data. In the latter, the investigators seek to apply methods developed from the theory part to resolve machine learning type problems that arise in biology and medicine. In particular, this team intends to study the problems related to biological and medical prediction in response to treatments, clinical diagnosis of diseases (such as cancers), discovery of protein-protein interactions and biological network constructions related to disease etiology and motif identification. To achieve these two goals, the investigators will study theoretical and practical properties under a general setting and evaluate a series of novel statistical/computation procedures/software which will then be tested by a broad range of real and simulated data, some from current on-going studies.The emergence of high dimensional data in most scientific fields poses new challenges for statisticians. Methods successful in dealing with low dimensional data are no longer effective for high dimensional data. One of the greatest difficulties in analyzing these data is to identify the informative variables/features and their associated clusters, and decipher the characteristics of the interaction between these variables and clusters. To meet current and future needs for digging hidden knowledge out of high dimensional data comprehensively and systematically, the scientific fields must develop new methods. The current project is a direct response to this need. Based on theoretical evidence (as preliminary results) already obtained in extracting low dimensional information, this team plans to apply and to develop various effective procedures to address practically important problems in the domains of biology and medicine. The investigators will study a novel screening process applicable across fields to demonstrate how high quality classifiers of low dimensionality can be identified while joint information among the influential variables are fully utilized. For further interpretation for biological validation/confirmation this team will study how to construct biological networks based on low dimensional classifiers and how to identify significant association patterns among them. A feedback mechanism will be established between the methodology development and biological validation teams, where statistical/computational results will be regularly discussed and biologically validated. It is anticipated that the key ideas and methods developed here will find numerous applications in disciplines other than biology/medicine. The proposed research is likely to advance substantial knowledge and significantly benefit current and future efforts in molecular biology/statistics/computational biology/disease prediction/drug discovery. The project would also provide valuable research experiences and training to undergraduates.

该应用程序介绍了一项全面的研究计划，以研究一般框架和各种新方法，以处理由生物学（医学）以及其他科学研究产生的复杂大规模数据集。该提案中阐明了两个目标：生物学和医学中的理论发展和应用。前者专注于研究一般但核心，无模型的框架，以有效解决高维数据引起的主要问题。在后者中，研究人员试图应用从理论部分开发的方法来解决生物学和医学中出现的机器学习类型问题。特别是，该团队打算研究响应治疗的生物学和医学预测，疾病的临床诊断（例如癌症），蛋白质 - 蛋白质相互作用的发现以及与疾病病因学和基序鉴定有关的生物网络结构的问题。为了实现这两个目标，研究人员将在一般环境下研究理论和实践属性，并评估一系列新颖的统计/计算程序/软件，然后通过广泛的真实和模拟数据进行测试，其中一些来自当前正在进行的研究。成功处理低维数据的方法不再对高维数据有效。分析这些数据的最大困难之一是确定信息性变量/特征及其相关群集，并破译这些变量和簇之间相互作用的特征。为了满足当前和未来的需求，可以全面和系统地从高维数据中挖掘隐藏的知识，科学领域必须开发新方法。当前项目是对此需求的直接响应。基于理论证据（作为初步结果）在提取低维信息方面已经获得，该团队计划申请并制定各种有效的程序，以解决生物学和医学领域的实际重要问题。研究人员将研究适用于跨领域的新型筛选过程，以证明如何确定低维度的高质量分类器，而在影响变量之间的联合信息得到了充分利用。为了进一步解释生物学验证/确认，该团队将研究如何基于低维分类器以及如何识别它们之间的重要关联模式来构建生物网络。方法开发与生物验证团队之间将建立反馈机制，在该团队中将定期讨论统计/计算结果并在生物学上进行验证。预计此处开发的关键思想和方法将在生物学/医学以外的其他学科中找到许多应用。拟议的研究可能会促进大量知识，并显着使当前和未来的努力在分子生物学/统计/计算生物学/疾病预测/药物发现方面。该项目还将为大学生提供宝贵的研究经验和培训。