Effective cancer subtyping by employing density peaks clustering by using gene expression microarray

Discovering the similar groups is a popular primary step in analysis of biomedical data, which cannot be identified manually. Many supervised and unsupervised machine learning and statistical approaches have been developed to solve this problem. Clustering is an unsupervised learning approach, which organizes the data into similar groups, and is used to discover the intrinsic hidden structure of data. In this paper, we used clustering by fast search and find of density peaks (CDP) approach for cancer subtyping and identification of normal tissues from tumor tissues. In additional, we also address the preprocessing and underlying distance matrix's impact on finalized groups. We have performed extensive experiments on real-world and synthetic cancer gene expression microarray data sets and compared obtained results with state-of-the-art clustering approaches.