Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques

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

Finding Informative Genes in Subtypes of Breast Cancer

World wide, one in nine women is diagnosed with breast cancer in her lifetime and breast cancer is the second leading cause of death among women. Accurate diagnosis of the specific subtypes of this disease is vital to ensure that the patients will have the best possible response to therapy. In this thesis, we use different machine learning techniques to select the most informative biomarkers for the recently proposed ten subtypes of breast cancer. Unlike existing gene selection approaches, we use a hierarchical based classification approach that selects genes and builds the classifier concurrently in a top-down fashion. We also propose a new bottom-up hierarchical approach to obtain the most informative genes for different subtypes, while we identify the similarity level between these subtypes. Our results support that this modified approach to gene selection yields a small subset of genes that can predict each of these ten subtypes with very high accuracy. The bottom-up approach, on the other hand, provides an insightful structure for further analysis of these subtypes

An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomic, Interactomic, and Transcriptomic Data

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 one subtype from the others with very high accuracy