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Integrative Machine Learning Approaches for Cancer Subtype and Survivability Prediction

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  • Mon, 06/18/2018 - 1:00pm - 3:00pm

Integrative Machine Learning Approaches for Cancer Subtype and Survivability Prediction

PhD. Thesis Proposal by:


Date: Monday, June 18, 2018
Time: 1:00 pm - 3:00 pm
Location: 3105, Lambton Tower

Abstract: Cancer is characterized by the uncontrolled cell growth or the loss of the ability to suicide as programmed or to stay in the tissue. The primary cause is that the genes, called diver genes, regulating cell growth and differentiation have been mutated. Studies on gene expression data can help uncover the mechanism of cancers. For example, gene expression has been used to predict the response of breast cancer patients after a specific treatment or to diagnose the patient’s subtype. The results of gene expression classification help 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. Furthermore, we can obtain better predictive biomarkers and better prediction results if we appropriately integrate other useful data such as copy number variation, mutation, protein-protein interaction, pathway data, with gene expression data into the learning and the validation processes. In this proposal, we briefly review some methods for subtype prediction and survivability prediction in cancer. And then, we propose new machine learning approaches that we investigate to improve the quality of biomarkers for cancer diagnosis and prognosis, including network-based machine learning. The approaches can be applied to any cancer data; however, we will mostly focus on breast cancer data.

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

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