Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

Abstract

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.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

“…The research established on individual cancer types (such as PCa) recommended that genes often share the same functional pathway, therefore, the association between cancer modules and functional connectivity has been suggested to be investigated [ 60 , 61 ]. The two modules generated from the DEGs network in this study revealed module-1 with a score of 31.9 which included 33 nodes/genes and 511 edges, and module-2 with a score of 6.0, which included 6 nodes/genes, and 13 edges.…”