1st International and 10th National Iranian Conference on Bioinformatics

Prediction of Disease-Causing Genes in Breast Cancer by Graph Mining in Biological Networks

Paper ID : 1264-ICB10

Authors:

Alireza Meshkin *, alireza molaei, khosro goudarzi

Department of Computer Engineering, Islamic Azad University, Damavand Branch, Damavand, Iran

Abstract:

Traditional studies in breast cancer, do not locate exactly the casual breast cancer genes in the genome and they often detect a region containing many candidates’ genes. Gene prioritization problem tries to rank the candidate genes from most to least promising. It can lead to faster discovery of novel casual genes and can lead to better diagnostic accuracy and treatment in breast cancer.
Despite many advances in medical science and biology, many people die each year from breast cancer. This shows that science still has a long way to go to cure this cancer. Breast cancer is a very complex and deadly disease. Many of the genes associated with the disease are not yet known, for example, known genes in breast cancer, such as BRCA1 and BRCA2, account for only 5% of breast cancer cases. In this study, protein network data source and network-based algorithms were evaluated and then Network propagation algorithm applied to prioritize candidate genes for breast cancer.
The results showed that protein networks have a significant impact on the quality of the gene prioritization approach. This approach outperformed previously published algorithms (e.g., DIR, and ENDEAVOUR) in evaluation metrices such as AUC, average rank, and TOP 5% metrics.

Keywords:

Candidate Gene Prioritization, Biological Complex Network, Graph mining, Breast Cancer, Network Propagation.

Status : Paper Accepted (Poster Presentation)