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Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning
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
doi:10.1109/ist48021.2019.9010510
dblp:conf/ist/HamoudiBAHNN19
fatcat:rng3wg3rafe6xdmylvyzpmoydm