Next Article in Journal
Lyndon Factorization Algorithms for Small Alphabets and Run-Length Encoded Strings
Previous Article in Journal
A Hybrid Autoencoder Network for Unsupervised Image Clustering
Open AccessReview

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Dipartimento di Scienze e Tecnologie, Università di Napoli Parthenope, 80133 Naples, Italy
Author to whom correspondence should be addressed.
Algorithms 2019, 12(6), 123;
Received: 25 May 2019 / Revised: 13 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
PDF [379 KB, uploaded 19 June 2019]


As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree reconstruction, protein secondary and tertiary structure prediction, protein function prediction, DNA microarray analysis, gene regulation/regulatory networks, just to mention a few, and an army of researchers, coming from several scientific backgrounds, focus their efforts on developing models to properly address these problems. In this paper, we aim to briefly review some of the huge amount of machine learning methods, developed in the last two decades, suited for the analysis of gene microarray data that have a strong impact on molecular biology. In particular, we focus on the wide-ranging list of data clustering and visualization techniques able to find homogeneous data groupings, and also provide the possibility to discover its connections in terms of structure, function and evolution. View Full-Text
Keywords: clustering; data visualization; gene expression data; data mining clustering; data visualization; gene expression data; data mining

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ciaramella, A.; Staiano, A. On the Role of Clustering and Visualization Techniques in Gene Microarray Data. Algorithms 2019, 12, 123.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top