A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability - Phạm, Quang Huy (ACM, 2023)
Identifying biomarkers for better diagnosis or prognosis of breast cancer is in demand but presents many challenges. In this study, we introduced a data-integration approach to identify sub-network biomarkers capable of predicting breast cancer treatment outcomes including disease-free survival, and overall survival at five years and long-term. A gene expression data is used for evaluating the predictive power of sub-networks of genes, while the protein-protein interaction network is to guide the search for the candidate sub-networks. To reduce the search space, we proposed a score to estimate the predictive ability of a set of genes, thus, only the candidates with the high score are evaluated by Support Vector Machine classifier during the search. After the sub-networks with the highest classification performance were selected for all seed genes, they were further analyzed with pathway data and cancer-related genes from literature for their biological meaning. The selected sub-networks yielded highly accurate and contained genes associated with many cancer pathways, including breast cancer.