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MSc Thesis Proposal Announcment of Li Zhou:"Classification of Breast Cancer Nottingham Prognostic Index using High-dimensional Embedding and Convolutional Neural Networks "

Thursday, May 6, 2021 - 13:00 to 15:00

SCHOOL OF COMPUTER SCIENCE 

The School of Computer Science is pleased to present… 

MSc Thesis Proposal by: Li Zhou 

 
Date:  Thursday May 6th, 2021 
Time:  1:00pm – 3:00pm 
Passcode:       If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca
 

Abstract:  

Nottingham Prognostics Index (NPI) is a widely used prognostics measure to predicting survival of operable primary breast cancer. The NPI value is calculated based on the size of the tumor, the number of lymph nodes, and the grade of the tumor. This work builds a prediction model for the NPI > 3.4 versus NPI < 3.4, where this threshold is the cut-off between high survival rate versus the low survival rate. Rapid development in next-generation sequencing led to the ability to measure different biological indicators that are called multi-omics data. The availability of multi-omics data sparked the challenge of integrating and analyzing these different biological measures to understand the development of the diseases. High-dimensional embedding techniques are used to present the features in the lower dimension such as a 2-dimensional map. A convolutional neural network (CNN) is a class of deep neural networks that are known in the image classification field. In this thesis, we propose a t-distributed stochastic neighbor embedding (t-SNE) method combined with CNN to integrate 2-dimensional maps from multi-omics datasets and predict the NPI score. 
 
Keywords: classification, data integration, multi-omics data, convolutional neural network. 
 

MSc Thesis Committee:  

Internal Reader: Dr. Jianguo Lu   
External Reader: Dr. Huapeng Wu             
Co-advisor: Dr. Luis Rueda 
Co-advisor: Dr. Abedalrhman Alkhateeb 
 
 

MSc Thesis Proposal Announcement  Vector Institute approved artificial intelligence approved topic

 

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