Increase the Accuracy of Detection of Pathogenic Genes of Breast Cancer using a Graph-Based Approach to the Gene Prioritization Problem

Cancer is one of the most common causes of mortality today. This disease's complications impose many costs on the human community's health, care, and well-being sectors. Solving complex biological problems requires advanced computational methods, and bioinformatics was created to solve such complex problems with the active interaction of several fields of science. Bioinformatics is an interdisciplinary science combining biological sciences, computers, mathematics, and statistics. The issue investigated in this research deals with one of the challenging issues in bioinformatics, namely candidate gene prioritization in breast cancer. Gene prioritization means sorting genes based on their relevance to a specific disease, such as breast cancer. Finally, the genes are checked according to their importance in performing costly experiments. The proposed approach in this research is to present a method based on graph mining for prioritizing genes. The study conducted with ENDEAOUR and DIR methods was compared and evaluated. The evaluation results show that the designed method is more efficient than other compared methods.