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MSc Thesis Proposal Announcement by Kowshik Sharan Subramanian:"Genetic Algorithm based Convolutional Neural Network for Few Shot Learning in Disease type prediction on RNA Seq data"

Wednesday, June 10, 2020 - 13:00 to 14:30

SCHOOL OF COMPUTER SCIENCE

The School of Computer Science is pleased to present…

 

MSc Thesis Proposal by:  Kowshik Sharan Subramanian

 
Date: Wednesday, June 10, 2020
 
Time:  1:00 PM – 2:30 PM
 
 

Abstract:

 
Diagnosing the correct types of the disease is essential to the effective treatment. The diagnosis may not always be straightforward from the biological tests especially during the early stages of the disease. Human body responds to the disease by producing certain proteins. If we know which genes are active, that is, which proteins are being produced, we can more accurately classify disease subtypes. This study is based on the genetic information extracted from the patient’s biological sample. Among different types of genetic data, we consider RNA-seq data in this thesis. Studies based on genetic information often suffer from very limited samples and few shot learning has recently been studied for disease classification. Given the success of neural networks in assisting data analysis mostly with large amounts of data, we perform few shot learning by retraining the neural networks with genetic algorithmic processes. We follow the proposal from the Human Genome Organization (HUGO) to group genes based on their chemical composition and apply genetic algorithms to the HUGO gene groups to help retrain the neural networks.
 
 

Thesis Committee:

 
Internal Reader: Dr. Sherif Saad Ahmed
 
External Reader: Dr. Myron Hlynka          
 
Advisor: Dr. Jessica Chen
 
 

MSc Thesis Proposal Announcement  

 

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