Novel Optimization-Based Biclustering Algorithms for Biomedical Data Analysis

用于生物医学数据分析的基于优化的新型双聚类算法

• 批准号: 0825993
• 负责人: Oleg Prokopyev
• 金额: $ 21.28万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0825993&HistoricalAwards=false
• 关键词: Novel; Optimization; Based; Biclustering; Algorithms

This award provides funding for the development of novel optimization-based methods for handling the problem of biclustering in biomedical data analysis. Biclustering consists of simultaneously partitioning the set of data samples and the set of their attributes (features) into subsets (clusters). Biclustering has great significance for data analysis in a variety of biomedical applications. Performing it with high reliability, we are able to not only diagnose conditions represented by sample clusters, but also to identify the key features (e.g., genes) responsible for them, or serving as their markers. The criteria used to relate clusters of samples and clusters of features may differ, relying on various patterns of interest among elements of a bicluster. The search for these patterns can be represented as optimization tasks, the solutions of which form the biclusters of data. We propose a family of new mathematical programming models to tackle the biclustering problem in biomedical data analysis. Advanced solution approaches will be developed for solving the proposed optimization models. Computational experiments involving real data analysis problems will be performed to validate the implemented algorithms against existing methodologies.If successful, the results of this research project are expected to have a high impact in the area at the intersection of optimization, data mining and biomedicine. It will facilitate the expansion of advanced biclustering analysis in related health care and biomedical applications, potentially improving prediction and diagnosis procedures, as well as the understanding of the mechanisms responsible for many diseases. The proposed work is also expected to enhance the existing computational tools and methodologies for solving hard optimization problems.

该奖项为生物医学数据分析中处理双聚类问题的新型优化方法的开发提供资金。双聚类包括将数据样本集及其属性（特征）集同时划分为子集（聚类）。双聚类在各种生物医学应用中的数据分析具有重要意义。以高可靠性执行它，我们不仅能够诊断样本集群所代表的条件，而且还能够识别负责它们的关键特征（例如，基因），或作为它们的标记。用于关联样本簇和特征簇的标准可能不同，这取决于双聚类元素之间的各种兴趣模式。对这些模式的搜索可以表示为优化任务，其解决方案形成数据的双聚类。我们提出了一组新的数学规划模型来解决生物医学数据分析中的双聚类问题。将开发先进的解决方法来解决所提出的优化模型。将进行涉及实际数据分析问题的计算实验，以验证针对现有方法的实现算法。如果成功，该研究项目的结果预计将在优化、数据挖掘和生物医学的交叉领域产生很大的影响。它将促进在相关保健和生物医学应用中扩大先进的双聚类分析，有可能改进预测和诊断程序，以及对导致许多疾病的机制的理解。提出的工作也有望提高现有的计算工具和方法，以解决困难的优化问题。