MICROARRAY GENE EXPRESSION BICLUSTERING USING ASSOCIATIVE PATTERN MINING

使用关联模式挖掘的微阵列基因表达双聚类

• 批准号: 7959474
• 负责人: PRERNA SETHI
• 金额: $ 4.22万
• 依托单位: LOUISIANA STATE UNIV A&M COL BATON ROUGE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7959474
• 关键词: Algorithms; Biochemical; Bioinformatics; Biological; Biomedical Research; Categories; Cell physiology; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Data; Data Set; Evaluation; Funding; Future; Gene Expression; Gene Expression Microarray Analysis; Genes; Grant; Histocompatibility Testing; Institution; International; Knowledge; Louisiana; Malignant Neoplasms; Measures; Methods; Mining; Molecular Medicine; Monitor; Pattern; Property; Proteins; Publishing; Regulation; Research; Research Personnel; Resources; Running; Sampling; Source; Time; United States National Institutes of Health; Validation; Work; base; design; gene discovery; research study; statistics; text searching

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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 生物信息学所面临的一个艰巨挑战是，为几个国际基因测序项目所发现的数千种迄今尚未表征的基因产物分配生物化学和细胞功能。同样，微阵列基因表达分析是计算机分子医学方法设计中的重要组成部分，可以同时监测不同样品（条件）下数千个基因的表达水平。从基因表达数据中提取具有生物学意义的知识是一个日益增长的计算挑战，因为大量的基因，其可以对应于不同的时间序列或组织类型，具有比所评估的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基因组，并共享相似的模式和生物学特性，如调控和功能。 我们根据基因的预测能力对基因进行选择和排名，通过对癌症数据集应用八种统计指标将样本分类为功能类别。然后研究了基因的排序集，以了解它们之间的关联。所发现的关联通过其相似性排名措施进行聚类，并通过运行几组实验来比较其功效。进行生物医学文献检索以研究所发现基因的功能注释。未来的工作涉及完成的双聚类算法与验证结果，使用已发表的聚类统计，信息增益模式，和专家评估发现的基因和相关条件，其生物学意义和表征。