Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning

Rifat Hamoudi, Meriem Bettayeb, Areej Alsaafin, Mahmood Hachim, Qassim Nassir, Ali Bou Nassif
2019 2019 IEEE International Conference on Imaging Systems and Techniques (IST)  
Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the
more » ... y to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data.
doi:10.1109/ist48021.2019.9010510 dblp:conf/ist/HamoudiBAHNN19 fatcat:rng3wg3rafe6xdmylvyzpmoydm