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

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Personal and Ubiquitous Computing Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; User Interfaces and Human Computer Interaction; Computer Science, general; Personal Computing; Mobile Computing
ISSN
1617-4909
eISSN
1617-4917
DOI
10.1007/s00779-018-1112-y
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Personal and Ubiquitous Computing – Springer Journals

Published: Feb 12, 2018

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