【摘要】 针对基因功能分类体系GeneOntology的层次结构特点 ,修改关联规则挖掘算法Apriori,开发“挖掘与基因差异表达关联的GO功能组合”软件 (RuleGO) .RuleGO以基因表达谱上的差异表达基因集合和不差异表达基因集合为输入 ,输出组合特征功能类与基因差异表达现象的关联规则 ,有助于解释基因差异表达现象的本质原因 ,如疾病发病机制、药物作用机理等 .将RuleGO和OntoExpress应用在结肠癌和腺癌表达谱数据集上 ,结果显示 ,RuleGO比OntoExpress能发现更多的与差异表达现象关联的特征功能类 ,更能看到在OntoExpress上不能发现的组合特征功能类 .另外 ,结果显示 ,将规则的置信度和支持度要求设置较高时 ,一般只有组合功能类才能满足要求 ,这提示在基因表达谱分析中不宜采用单个角度的单个功能分类单元 ,考虑功能分类单元的组合可能更有意义更多还原

【Abstract】 To adapt to the hierarchical structural property of Gene Ontology, the standard Apriori algorithm is modified into a novel algorithm, RuleGO, which mines association rules of GO function classes and gene expression difference. The inputs of RuleGO are one set of differential expressed genes and another set of non-differential expressed genes, and the outputs of RuleGO are association rules linking GO function combinations to gene differential expression. Rules mined by RuleGO may guide insights into gene expression difference at the functional level, towards the clarification of the process of pathological changes or the mechanism of medicine. Both RuleGO and OntoExpress are applied to the datasets of colon cancer and adenocarcinoma, and RuleGO turned out to be more powerful to mine relevant function rules than OntoExpress. The experimental results also reveal that rules with both high significance and high support mostly involve more than one gene function classes, suggesting that considering the combination of multiple gene function classes may be more resonable in gene expression analysis than taking into account only a single gene function class.更多还原