MICROARRAY GENE EXPRESSION BICLUSTERING USING ASSOCIATIVE PATTERN MINING

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 生物信息学面临的艰巨挑战之一是，如何为几个国际基因测序项目发现的数千种迄今尚未确定特征的基因产品分配生化和细胞功能。同样，微阵列基因表达分析是电子分子医学方法设计中的一个重要组成部分，它使同时监测不同样本(条件)下数千个基因的表达水平成为可能。从基因表达数据中提取生物学上有意义的知识是一个日益增长的计算挑战，因为可以对应于不同时间序列或组织类型的大量基因具有比评估样本多几个数量级的维度。一个重要的分析目标是识别相关的、共享相似模式和生物学特性的基因集，如调节和功能。 我们选择了这些基因，并根据它们的预测能力对样本进行了排序，通过对癌症数据集应用八个统计指标来将样本分类为功能类别。然后对排序后的基因集进行研究，以寻找它们之间的关联。发现的关联通过它们的相似性排名度量进行分类，并通过运行几组实验来比较它们的有效性。进行生物医学文献检索，研究已发现基因的功能注释。未来的工作包括完成双簇算法，并使用已公布的簇统计数据、信息增益模式和对已发现的基因及其生物学意义和特征的相关条件进行专家评估来验证结果。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. One of the daunting challenges facing bioinformatics is to assign biochemical and cellular functions to the thousands of hitherto uncharacterized gene products discovered by several international gene-sequencing projects. Similarly, microarray gene expression analysis, an important component in the design of in-silico molecular medicine methods, has made possible to monitor the expression level of thousands of genes under different samples (conditions) at the same time. Extraction of biologically significant knowledge from the gene expression data is a growing computational challenge as the large number of genes, which can correspond to different time sequences or tissue types, have a dimensionality that is several orders of magnitude more than the evaluated samples. An important analysis aim is to identify sets of genes that are correlated, and share similar pattern and biological properties such as regulation and function. We selected and ranked the genes based on their predictive power to classify samples into functional categories by applying eight statistical measures on a cancer dataset. The ranked sets of genes were then studied for the associations between them. The discovered associations were clustered by their similarity ranking measures and compared for their efficacy by running several sets of experiments. A biomedical literature search was conducted to study the functional annotation of the discovered genes. The future work involves the accomplishment of the biclustering algorithm with validation results using published cluster statistics, information gain schemas, and expert evaluation of the discovered genes and relevant conditions for their biological significance and characterization.