Novel Optimization-Based Biclustering Algorithms for Biomedical Data Analysis

该奖项为开发新的基于优化的方法提供资金，用于处理生物医学数据分析中的双聚类问题。双聚类包括同时将数据样本集及其属性（特征）集划分为子集（聚类）。双聚类对于生物医学领域的数据分析具有重要意义。以高可靠性执行它，我们不仅能够诊断由样本聚类表示的条件，而且还能够识别关键特征（例如，他们的行为，或他们的行为，或他们的行为。用于关联样本聚类和特征聚类的标准可能不同，这取决于双聚类元素之间的各种感兴趣模式。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。我们提出了一个新的数学规划模型，以解决生物医学数据分析中的双聚类问题。将开发高级求解方法来求解提出的优化模型。将进行涉及真实的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重大影响。它将有助于在相关的医疗保健和生物医学应用中扩展先进的双聚类分析，可能改善预测和诊断程序，以及了解许多疾病的机制。所提出的工作也有望提高现有的计算工具和方法来解决困难的优化问题。

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.