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Article

Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines

1
Department of Informatics, University of Bari “Aldo Moro”, Via Orabona 4, 70125 Bari, Italy
2
Department of Pharmacy-Pharmaceutical Sciences, University of Bari “Aldo Moro”, Via Orabona 4, 70125 Bari, Italy
3
Department of Biomedical Sciences and Human Oncology, Internal Medicine Unit G. Baccelli, University of Bari Aldo Moro Medical School, 70125, Bari, Italy
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5552; https://doi.org/10.3390/app9245552
Received: 23 October 2019 / Revised: 5 December 2019 / Accepted: 10 December 2019 / Published: 17 December 2019
(This article belongs to the Section Applied Biosciences and Bioengineering)
Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. Since the number of genes in DNA is huge, they are usually high dimensional, therefore they require dimensionality reduction and clustering techniques to extract useful information. In this paper we use NMF, non-negative matrix factorization, to analyze microarray data, and also develop “intelligent” results visualization with the aim to facilitate the analysis of the domain experts. For this purpose, a case study based on the analysis of the gene expression profiles (GEPs), representative of the human multiple myeloma diseases, was investigated in 40 human myeloma cell lines (HMCLs). The aim of the experiments was to study the genes involved in arachidonic acid metabolism in order to detect gene patterns that possibly could be connected to the different gene expression profiles of multiple myeloma. NMF results have been verified by western blotting analysis in six HMCLs of proteins expressed by some of the most abundantly expressed genes. The experiments showed the effectiveness of NMF in intelligently analyzing microarray data. View Full-Text
Keywords: nonnegative matrix factorization; intelligent data analysis; feature extraction; dimensionality reduction; unsupervised learning; human multiple myeloma cell lines; gene expression profile; arachidonic acid metabolism nonnegative matrix factorization; intelligent data analysis; feature extraction; dimensionality reduction; unsupervised learning; human multiple myeloma cell lines; gene expression profile; arachidonic acid metabolism
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MDPI and ACS Style

Casalino, G.; Coluccia, M.; Pati, M.L.; Pannunzio, A.; Vacca, A.; Scilimati, A.; Perrone, M.G. Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines. Appl. Sci. 2019, 9, 5552. https://doi.org/10.3390/app9245552

AMA Style

Casalino G, Coluccia M, Pati ML, Pannunzio A, Vacca A, Scilimati A, Perrone MG. Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines. Applied Sciences. 2019; 9(24):5552. https://doi.org/10.3390/app9245552

Chicago/Turabian Style

Casalino, Gabriella, Mauro Coluccia, Maria L. Pati, Alessandra Pannunzio, Angelo Vacca, Antonio Scilimati, and Maria G. Perrone 2019. "Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines" Applied Sciences 9, no. 24: 5552. https://doi.org/10.3390/app9245552

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