Hamoudi, R; Bettayeb, M; Alsaafin, A; Hachim, M; Nassir, Q; Nassif, AB; (2019) Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning. In: Proceedings of 2019 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE: Abu Dhabi, United Arab Emirates.

Preview

Text Hamoudi_IEEE BC Final.pdf - Accepted Version Download (1MB) | Preview

Abstract

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 ability 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.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item