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

Fei Xie,Jianing Xi,Qun Duan,Jianxin Li

doi:10.1155/2020/6643551

Fei Xie, Jianing Xi + Show 2 more

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https://doi.org/10.1155/2020/6643551

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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.

Highlights

Gene performs a function via the synthesis of its product protein encoded by the gene in human cells, and the interactions between proteins lead to the functional cooperation between different genes [1]
In addition to the network data, we incorporate the mutation data of sequencing samples from two distinct types of cancer, prostate cancer [32] and thyroid cancer [33]. e mutation data of both types of cancers are accessed from the cBioPortal database [34], which offers a web resource for cancer genomics data. e cancer driver attribute annotations of genes are collected from the COSMIC Cancer Gene Census database [35], which provides well-curated cancer driver genes that have been widely acceptable in tumorigenesis field
There are many successes achieved by previous methods based on network properties of gene nodes and network propagation of mutation frequencies, there are still obstacles in the task of cancer driver attribute filling of genes

Summary

Introduction

Gene performs a function via the synthesis of its product protein encoded by the gene in human cells, and the interactions between proteins lead to the functional cooperation between different genes [1]. The aberrations of gene can alter the function of the gene itself and influence the functions of other genes that interact with the aberrated gene, and both ways can lead to carcinogenesis of normal cells [2, 3]. When the network topology is investigated, it is observed that gene interaction network supports the property of being scale-free, which fairly satisfies the definition of complex network [4]. Through the network properties such as centrality and betweenness involved in complex network analytics [5, 6], it is an unprecedent opportunity to attributes of genes by investigating their roles in interaction network

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