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首页> 外文会议>2013 8th International Symposium on Health Informatics and Bioinformatics >A genetic algorithm approach to active subnetwork search applied to GWAS data
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A genetic algorithm approach to active subnetwork search applied to GWAS data

机译：GWAS数据的一种主动子网搜索的遗传算法

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摘要

An active subnetwork is a group of interconnected genes that show condition-specific differences. It has been observed that the gene products that have alterations associated with a disease of interest, incline to be part of the subnetworks among the overall interaction network. Hence, the integration of the interaction data with the genotypic data underlying disease states facilitates the separation of the subnetworks perturbed in a given disorder from the rest of the network. In the literature, active subnetwork search is used to discover disease related regulatory pathways, dysregulated genes, functional modules, cancer markers, to classify diseases, and to predict response to treatment. In this study, a genetic algorithm based method is developed for active subnetwork search and applied to WTCCC Rheumatoid Arthritis genome-wide association study dataset. The relevance of the identified subnetworks against the disease is compared in terms of biological pathways. Our results show that the proposed method works well in detecting the significant RA associated subnetworks, and it is also applicable to recognize subnetworks of other complex diseases.
机译：一个活跃的子网是一组相互关联的基因，它们显示条件特定的差异。已经观察到，具有与目的疾病相关的改变的基因产物倾向于成为整个相互作用网络中子网络的一部分。因此，将交互作用数据与疾病状态下的基因型数据进行整合有助于将在给定疾病中受到干扰的子网与网络的其余部分分离。在文献中，主动子网搜索用于发现疾病相关的调节途径，失调的基因，功能模块，癌症标志物，对疾病进行分类并预测对治疗的反应。在这项研究中，开发了一种基于遗传算法的主动子网搜索方法，并将其应用于WTCCC类风湿关节炎全基因组关联研究数据集。根据生物学途径比较已识别子网与疾病的相关性。我们的结果表明，该方法在检测与RA相关的重要子网络方面效果很好，并且还可以用于识别其他复杂疾病的子网络。

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