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MSc Thesis Defense Announcement of Kowshik Sharan Subramanian:"Genetic Algorithm based Convolutional Neural Network for Few Shot Learning in Disease type prediction on RNA-Seq Data "

Thursday, October 8, 2020 - 13:00 to 14:30

The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Kowshik Sharan Subramanian 

 
Date: Thursday October 8th, 2020 
 
Time:  1:00pm – 2:30pm 
 
 

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 and is used to classify cancer subtypes. 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. We apply our proposed approach to several different cancer datasets and compare our method across state-of-the-art methods. We have observed that our proposed approach performs better and has a better weighted average score than the previously proposed Affinitynet for few-shot learning with small datasets. 
 
Keywords: Genetic Algorithms, Convolutional neural networks, cancer subtype classification, HUGO Gene groups, RNA Seq, Few-shot learning 
 
 

Thesis Committee:  

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

MSc Thesis Defense Announcement Vector Institute of Artificial Intelligence approved logo

 

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