Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques
Date of Award
Network biomarkers, Breast cancer subtypes, Repurposed drugs, Machine learning
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies. Gathering knowledge about the functional relationship among genes will identify “network biomarkers” that will enrich the criteria for biomarker selection. Cancer network biomarkers are subnetworks of functionally related genes that “work in concert” to perform functions associated with a tumorigenic. We propose a machine learning framework that can be used to identify network biomarkers and driver genes for each specific breast cancer subtype. Our results show that the resulting network biomarkers can separate onesubtype from the others with very high accuracy. We also propose an integrated approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes.
Firoozbakht, Forough, "Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques" (2022). Electronic Theses and Dissertations. 9024.