Title:Application of the Shortest Path Algorithm for the Discovery of Breast Cancer-Related Genes
VOLUME: 11 ISSUE: 1
Author(s):Lei Chen, Zhi Hao Xing, Tao Huang, Yang Shu, GuoHua Huang and Hai-Peng Li*
Affiliation:CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China.
Keywords:Betweenness, breast cancer, disease gene, protein-protein interaction, shortest path algorithm, weighted network.
Abstract:Breast cancer, the most prevalent cancer in women, develops from breast tissue. Its incidence has increased in
recent years due to environmental risk factors. Thus, it is urgent to uncover the mechanism underlying breast cancer to
design effective treatments. Identification of all breast cancer-related genes is one way to help elucidate the underlying
breast cancer mechanism. In this study, a computational method was built and applied to discover new candidate breast
cancer-related genes. Based on the known breast cancer-related genes retrieved from public databases, the shortest path
algorithm was applied to discover new candidate genes in the protein-protein interaction network. The analysis results of
the selected genes suggest that some of them are deemed breast cancer-related genes according to the most recent
published literature, while others have direct or indirect associations with the initiation and development of breast cancer.
