DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network

Document Type : Research Paper


1 Department of information technology, School of Systems and Industrial Engineering, Tarbiat Modares University (TMU), Tehran, Iran

2 School of Systems and Industrial Engineering, Tarbiat Modares University (TMU), Chamran/Al-e-Ahmad Highways Intersection, Tehran, Iran

3 Department of Computer and Information Technology, Payame Noor University (PNU), P.O. Box, 19395-3697, Tehran, Iran


Background: Cancer is a group of diseases that have received much attention in biological research because of its high 
mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify 
cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the 
identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using 
networking techniques and the concept of influence maximization, some models have been proposed to identify these 
Objectives: We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a 
network science approach without the use of mutation and genomic data.
Materials and Methods: In this study, we will employ the social influence network theory to identify CDGs in the human 
gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create 
GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs.
Results: The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method can identify many CDGs that are overlooked by all previously published methods. 
Conclusions: Our study demonstrated that the Google’s PageRank algorithm can be utilized and modified as a network based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.


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