GOALI: Robust and Efficient Knowledge Discovery With Application in Gene Expression Based Diagnosis

该奖项的研究目标是开发一种鲁棒且高效的方法，从高维、非比例类分布、不同时间不同来源的数据集中发现知识。其关键技术是一种无模型的知识发现方法，该方法不需要传统数据挖掘中耗时的模式提取步骤。这是通过离散化、特征选择和数据汇总将大型数据集转换为紧凑的列联表来实现的。采用基于混淆矩阵的数据集特征度量来保证特征选择对类分布的鲁棒性，从而在约简数据集中保持适当的类可分性。当有新数据可用时，可以有效地更新列联表以支持增量知识发现。开发的方法将被验证并应用于基于基因表达的诊断，这涉及到极高维度的数据集（高达数十万）。如果成功，本研究的结果将提供一种知识发现工具，使领域专家能够在各种应用中做出更好的决策，包括医疗保健中的循证医学、维修中的故障诊断以及金融和零售中的客户关系管理。基于基因表达诊断的目标应用，如果成功，将允许医生识别复杂的遗传特征，这些特征是不同表型、疾病亚型以及临床结果的基础。这将使个性化干预能够最大限度地提高个体患者的治疗效果。与医学院和辛辛那提儿童基金会的合作？s Hospital使访问关键领域专业知识和专门计算资源成为可能；从而加强研究和教育基础设施。研究成果将纳入工程和医学研究生课程，使学生能够受益于最新的跨学科研究进展。

The research objective of this award is to develop a robust and efficient method to discover knowledge from large data sets that are high dimensional, with disproportional class distribution, and with data coming from different sources at different times. The key technique is a model-free knowledge discovery method that does not require the time-consuming pattern extraction step traditionally carried out in data mining. This is accomplished by converting a large data set into a compact contingency table through discretization, feature selection, and data summarization. A confusion matrix based data set characteristic measure is used to ensure that feature selection is robust to class distribution so proper class separability is maintained in the reduced data set. When new data is available, the contingency table can be efficiently updated to enable incremental knowledge discovery. The developed method will be validated and applied to gene expression based diagnosis, which involves datasets with extremely high dimensionality (up to several hundred thousands). If successful, the results of this research will provide a knowledge discovery tool that enables domain experts to make better decisions in various applications including evidence-based medicine in healthcare, fault diagnosis in maintenance, and customer relationship management in finance and retail. The target application of gene expression based diagnosis, when successful, would allow physicians to identify complex genetic traits that underlie different phenotypes, disease subtypes, as well as clinical outcome. This will enable personalized intervention to maximize treatment effectiveness for individual patients. The collaboration with College of Medicine and Cincinnati Children?s Hospital makes it possible to access critical domain expertise and specialized computing resources; thus enhancing research and education infrastructure. Research results will be incorporated into both Engineering and Medicine graduate courses so students can benefit from most recent interdisciplinary research advances.