DPCCG-EJA: Detection of key Pathways and Cervical Cancer related Genes using Enhanced Johnson’s Algorithm

Abstract

In recent decade, cervical cancer is the second most predominant cancer seen in woman and over two lakhs deaths estimated annually. In this research, a new shortest path approach, Enhanced Johnson’s Algorithm (EJA) has been proposed for finding the cervical cancer related genes in Protein to Protein Interaction (PPI) network for the early diagnosis of cervical cancer. The PPI network was created based on the PPI data, which was collected from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) dataset. In this research study, totally 15 pre-invasive and 10 invasive genes were extracted from the STRING dataset utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Besides, shortest path between pre-invasive and invasive genes were identified using EJA. In EJA, the bellman ford approach reconstructs the path with a new iterative matrix was utilized for removing the negative cycles that effectively lessens the elapsed time by removing the negative cycle in gene connection. In the experimental phase, the proposed approach reduced the elapsed time up to 0.003-0.48 seconds related to the existing approaches. The proposed algorithm detects new biomarkers in the pathways. These biomarkers detect the genes responsible for cervical cancer.