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Identifying Breast Cancer biomarkers for Subtype and Survivability Prediction by Machine Learning and Data mining

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  • Thu, 12/14/2017 - 1:00pm - 3:00pm

Identifying Breast Cancer biomarkers for Subtype and Survivability Prediction by Machine Learning and Data mining

PhD. Comprehensive Exam by:


Date: Thursday, December 14th
Time: 1:00 pm – 3:00 pm
Location: 3105, Lambton Tower

Abstract. Breast cancer is the second leading causes of death among women cancers in USA and Canada. Although the 5-year survival rate is high and keeps increasing, the therapies for breast cancer still need to be improved. Studies on gene expression data have been conducted to help uncover the mechanism of the disease. For example, gene expression has been used to predict the response of breast cancer patients treated with chemotherapy, hormone therapy, or radiation. Gene expression has also been used to diagnose the patient’s subtype, such as PAM50 subtype, Claudin-subtype; here, the results of gene expression classification helps to decide which therapy to select. Machine learning and data mining are among the preferred and effective approaches to discover the gene signatures/biomarkers for those treatment response/subtypes prediction than other methods. Currently, there are many biological information published that also can be used for analyzing breast cancer, such as protein-protein interaction data and pathway data, and other “omics” data. We can obtain better predictive biomarkers and better prediction results (i.e, diagnosis or survivability) if we can appropriately integrate these “omics” data with gene expression data into the learning and the validation processes. Network-based machine learning approaches allow to integrate different types or sources of omics data. In this presentation, we briefly review the methods of breast cancer biomarker detection, subtype prediction, survivability prediction, and new machine learning approaches to breast cancer analysis, including network-based machine learning.

Thesis Committee:    
Internal Readers:  Dr. Asish Mukhopadhayay and Dr. Medhi Kargar
External Reader:    Dr. Lisa Porter
Advisors:              Dr. Alioune Ngom and Dr. Luis Rueda

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