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首页> 外文期刊>Network Modeling Analysis in Health Informatics and Bioinformatics >Identifying dense subgraphs in protein-protein interaction network for gene selection from microarray data
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Identifying dense subgraphs in protein-protein interaction network for gene selection from microarray data

机译：识别蛋白质-蛋白质相互作用网络中的密集子图以从微阵列数据中选择基因

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摘要

Selection of important genes responsible for a disease is an important task in bioinformatics. Microarray data are often used with differential expression being considered as a cue. Recently, such expression data are supplemented by gene ontology and genes/proteins interaction network for the selection task. The functional knowledge and interaction structure have become critical for understanding the biological processes, including selection of genes potentially associated to complex diseases. In this paper, we propose an approach that combines expression analysis with structural analysis of protein-protein interaction networks to identify genes associated with complex diseases. The dense subgraph structures embedded in the networks are extracted. We present results on three different types of benchmark cancer dataset (prostate cancer, interstitial lung disease and chronic lymphocytic leukemia) and show that several interesting biological information could be inferred, besides achieving a high prediction accuracy. The proposed methodology helps to identify not just differentially expressed genes but also hub genes important in biological processes.
机译：选择引起疾病的重要基因是生物信息学中的重要任务。通常使用微阵列数据，将差异表达视为提示。最近，这种表达数据通过基因本体论和基因/蛋白质相互作用网络得到补充，用于选择任务。功能知识和相互作用结构对于理解生物学过程（包括选择可能与复杂疾病相关的基因）已变得至关重要。在本文中，我们提出了一种将表达分析与蛋白质-蛋白质相互作用网络的结构分析相结合的方法，以鉴定与复杂疾病相关的基因。提取嵌入在网络中的密集子图结构。我们介绍了三种不同类型的基准癌症数据集（前列腺癌，间质性肺病和慢性淋巴细胞性白血病）的结果，结果表明，除了获得较高的预测准确性外，还可以推断出一些有趣的生物学信息。所提出的方法不仅有助于鉴定差异表达的基因，而且还有助于鉴定在生物学过程中重要的集线器基因。

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