  Identification of genes that initiate cell anomalies and cause cancer in
humans is among the important fields in the oncology researches. The mutation
and development of anomalies in these genes are then transferred to other genes
in the cell and therefore disrupt the normal functionality of the cell. These
genes are known as cancer driver genes (CDGs). Various methods have been
proposed for predicting CDGs, most of which based on genomic data and based on
computational methods. Therefore, some researchers have developed novel
bioinformatics approaches. In this study, we propose an algorithm, which is
able to calculate the effectiveness and strength of each gene and rank them by
using the gene regulatory networks and the stochastic analysis of regulatory
linking structures between genes. To do so, firstly we constructed the
regulatory network using gene expression data and the list of regulatory
interactions. Then, using biological and topological features of the network,
we weighted the regulatory interactions. After that, the obtained regulatory
interactions weight was used in interaction structure analysis process.
Interaction analysis was achieved using two separate Markov chains on the
bipartite graph obtained from the main graph of the gene network. To do so, the
stochastic approach for link-structure analysis has been implemented. The
proposed algorithm categorizes higher-ranked genes as driver genes. The
efficiency of the proposed algorithm, regarding the F-measure value and number
of identified driver genes, was compared with 23 other computational and
network-based methods.
