Document Type : Original Research Paper

Authors

• M. Akhavan-Safar ¹
• B. Teimourpour ²
• M. Ayyoubi ³

¹ Department of Computer and Information Technology Engineering, Payame Noor University (PNU), Tehran, Iran.

² Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran

³ Department of Data Science, Tarbiat Modares University (TMU), Tehran, Iran.

Abstract

Background and Objectives: One of the important topics in oncology treatment and prevention is the identification of genes that initiate cancer in cells. These genes are known as cancer driver genes (CDGs). Identification of the CDGs is important both for a basic understanding of cancer and to help find new therapeutic or biomarker goals. Several computational methods to find the genes responsible for cancer have been developed based on genome data. However, many of these methods find key mutations in genomic data to predict which genes are responsible for cancer. These methods depend on the mutation and genome data and often show a high rate of false positives in the results. In this study, we proposed an influence maximization-based approach, CinfuMax, which can detect the genes responsible for cancer without needing information on mutations.
Methods: In this method, the concept of influence maximization and the independent cascade model are employed. Firstly, the gene regulatory network for breast, lung and colon cancers was built using regulatory interactions and gene expression data. Next, we implemented an independent cascade diffusion algorithm on the networks to compute each gene's coverage. Finally, the genes with the highest coverage were classified as driver.
Results: The results of the proposed method were compared to 19 other computational and network-based methods based on the F-measure and the number of detected driver genes. The results demonstrated that the proposed method produces better results than other methods. Also, CinfuMax is able to detect 18, 19 and 22 individual driver genes in three breast, lung and colon cancers, respectively, which have not been identified in any of the previous methods.
Conclusion: The results show that independent cascading methods to identify driver genes perform better than linear threshold methods. Driver genes are also classified in terms of influence speed and have identified the genes with the highest diffusion rate in each type of cancer. Identification of these genes can be useful for molecular therapies and drug purposes.

Keywords

• Cancer Drivers
• Influence Maximization
• Independent Cascade Model
• Influence Speed
• Regulatory Networks

Main Subjects

• Bioinformatics

Open Access

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Shahid Rajaee Teacher Training University