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

Dipartimento di Scienze e Tecnologie, Università di Napoli Parthenope, 80133 Naples, Italy
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Algorithms 2019, 12(6), 123; https://doi.org/10.3390/a12060123
Received: 25 May 2019 / Revised: 13 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
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Abstract

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

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