Date: Thursday October 8th, 2020 

 

Time:  1:00pm – 2:30pm 

 

Zoom URL: https://zoom.us/j/92520665516? 

 

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 

 

 

Internal Reader: Dr. Sherif Saad Ahmed  

 

External Reader: Dr. Myron Hlynka 

 

Advisor: Dr. Jessica Chen 

 

Chair: Dr. Xiaobu Yuan