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Using Machine Learning Approaches to Identify Biomarkers in Breast Cancer Subtypes

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  • Wed, 06/27/2018 - 1:00pm - 3:00pm

Using Machine Learning Approaches to Identify Biomarkers in Breast Cancer Subtypes

PhD. Thesis Proposal by:

Forough Firoozbakht

Date:Wednesday, June 27, 2018

Time:1:00 pm - 3:00 pm
Location: 3105, Lambton Tower

Abstract: Breast cancer is a complex disease that can be classified into different molecular subtypes. Diagnosis and treatment of each specific subtype is critical for ensuring the best possible outcome. We use machine learning techniques along with information about functional relationships among genes to identify ‘‘network biomarkers’’ that will enrich the criteria for biomarker selection and driver genes for each specific breast cancer subtype. We obtain network biomarker corresponding to each breast cancer subtype that can distinguish each subtype from the rest with very high accuracy.

Moreover, we propose a machine learning approach for finding new potential treatment applications of the known drugs for each breast cancer subtype. Using this approach, we expect to find novel use for known drugs in treatment of each breast cancer subtype.

Thesis Committee:     
Internal Readers: Dr. Jianguo Lu and Dr. Dan Wu
External Reader: Dr. Esam Abdel-Raheem (Electrical and Computer Engineering)
Advisors: Dr. Luis Rueda and Dr. Alioune Ngom                


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