首页> 外文会议>Pacific Symposium on Biocomputing 2001, Jan 3-7, 2001, Mauna Lani, Hawaii >PREDICTING GENE FUNCTION FROM GENE EXPRESSIONS AND ONTOLOGIES
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PREDICTING GENE FUNCTION FROM GENE EXPRESSIONS AND ONTOLOGIES

机译:从基因表达和本体预测基因功能

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We introduce a methodology for inducing predictive rule models for functional classification of gene expressions from microarray hybridisation experiments. The basic learning method is the rough set framework for rule induction. The methodology is different from the commonly used unsupervised clustering approaches in that it exploits background knowledge of gene function in a supervised manner. Genes are annotated using Ashburner's Gene Ontology and the functional classes used for learning are mined from these annotations. From the original expression data, we extract a set of biologically meaningful features that are used for learning. A rule model is induced from the data described in terms of these features. Its predictive quality is fine-tuned via cross-validation on subsets of the known genes prior to classification of unknown genes. The predictive and descriptive quality of such a rule model is demonstrated on the fibroblast serum response data previously analysed by Iyer et. al. Our analysis shows that the rules are capable of representing the complex relationship between gene expressions and function, and that it is possible to put forward high quality hypotheses about the function of unknown genes.
机译:我们介绍了一种从微阵列杂交实验中诱导基因表达功能分类的预测规则模型的方法。基本的学习方法是规则归纳的粗糙集框架。该方法不同于常用的无监督聚类方法,因为它以监督方式利用了基因功能的背景知识。使用Ashburner的基因本体对基因进行注释,并从这些注释中提取用于学习的功能类。从原始表达数据中,我们提取了一组用于学习的生物学上有意义的特征。从根据这些特征描述的数据中得出规则模型。在对未知基因进行分类之前,可通过对已知基因的子集进行交叉验证来微调其预测质量。 Iyer等人先前分析的成纤维细胞血清反应数据证明了这种规则模型的预测性和描述性。等我们的分析表明,该规则能够代表基因表达与功能之间的复杂关系,并且有可能针对未知基因的功能提出高质量的假设。

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