The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.

中文翻译：

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通过基于模块化子空间的小样本概念学习，交互网络中基因的驱动程序属性填充

基因的畸变会影响基因及其邻近基因在基因相互作用网络中的功能，从而导致正常细胞发生癌变。考虑到基因相互作用网络是一个复杂的网络，以前的研究已经通过节点的网络特性和突变的网络传播对基因的驱动属性进行了努力。但是，仍然存在癌症样品尺寸小和没有网络邻居属性的驱动程序存在的问题，这限制了癌症驱动程序基因的发现。为了解决这些障碍，我们提出了一种有效的基于模块化子空间的概念学习模型。我们的模型可以通过属性概念学习任务中的维数减少来克服小样本导致的维数诅咒，并通过网络邻居之外的模块化子空间探索基因的特征。评估分析还证明了我们的模型在两个基因交互网络上的驾驶员属性填充任务中的优越性。通常，我们的模型在肿瘤发生相互作用网络分析的应用中显示出有希望的前景。