Network motif-based identification of breast cancer susceptibility genes.
Identifying breast cancer susceptibility genes is one of the key challenges in breast cancer research. Conventional gene-based approaches can identify patterns of gene activity that sub-classify tumors, by which genes with known breast cancer mutations are typically not detected. In this study, we present a novel network motif-based approach that integrates biological network topology and high-throughput gene expression data to identify markers not as individual genes but as network motifs. We observed that the network motifs are more reproducible than individual marker genes selected without biological network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
Zhang
Yuji
Y
Department of Electrical and Computer Engineering, Advanced Research Institute, Virginia Polytechnic Institute and State University, 4300 Wilson Blvd, Arlington, 22203, USA.
Xuan
Jianhua
J
de Los Reyes
Benilo G
BG
Clarke
Robert
R
Ressom
Habtom W
HW
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004198:Disease Susceptibility; D005260:Female; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins