{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:35:54Z","timestamp":1769722554762,"version":"3.49.0"},"reference-count":75,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a12060123","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T02:42:46Z","timestamp":1560912166000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["On the Role of Clustering and Visualization Techniques in Gene Microarray Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5592-7995","authenticated-orcid":false,"given":"Angelo","family":"Ciaramella","sequence":"first","affiliation":[{"name":"Dipartimento di Scienze e Tecnologie, Universit\u00e0 di Napoli Parthenope, 80133 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4708-5860","authenticated-orcid":false,"given":"Antonino","family":"Staiano","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze e Tecnologie, Universit\u00e0 di Napoli Parthenope, 80133 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","unstructured":"Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining, The MIT Press."},{"key":"ref_2","unstructured":"Staiano, A., De Vinco, L., Ciaramella, A., Raiconi, G., Tagliaferri, R., Longo, G., Miele, G., Amato, R., Del Mondo, C., and Donalek, C. (2004, January 1\u20134). Probabilistic principal surfaces for yeast gene microarray data-mining. Proceedings of the ICDM\u201904 Fourth IEEE International Conference on Data Mining Brighton (UK), Brighton, UK."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1016\/j.ins.2010.07.004","article-title":"A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients","volume":"180","author":"Calcagno","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_4","first-page":"954598","article-title":"Statistical and computational methods for genetic diseases: An overview","volume":"2015","author":"Camastra","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.mcp.2014.10.002","article-title":"Association of USF1 and APOA5 polymorphisms with familial combined hyperlipidemia in an Italian population","volume":"29","author":"Staiano","year":"2015","journal-title":"Mol. Cell. Probes"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-3-642-35467-0_18","article-title":"Investigation of single nucleotide polymorphisms associated with familial combined hyperlipidemia with random forests","volume":"19","author":"Staiano","year":"2013","journal-title":"Neural Nets Surround."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.1016\/j.cor.2012.03.008","article-title":"Clustering of High Throughput Gene Expression Data","volume":"39","author":"Pirim","year":"2012","journal-title":"Comput. Oper. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1002\/cfg.169","article-title":"Studying the Functional Genomics of Stress Responses in Loblolly Pine with the Expresso Microarray Experiment Management System","volume":"3","author":"Heath","year":"2002","journal-title":"Comp. Funct. Genom."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1038\/nbt1296-1675","article-title":"Expression Monitoring by Hybridization to High-Density Oligonucleotide Arrays","volume":"14","author":"Lockhart","year":"1996","journal-title":"Nat. Biotechnol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1126\/science.270.5235.467","article-title":"Quantitative Monitoring of Gene Expression Patterns with a Compolementatry DNA Microarray","volume":"270","author":"Schena","year":"1995","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/S0025-6196(11)62260-X","article-title":"Primer on Medical Genomics Part III: Microarray Experiments and Data Analysis","volume":"77","author":"Tefferi","year":"2002","journal-title":"Mayo Clin. Proc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1109\/TKDE.2004.68","article-title":"Cluster Analysis for Gene Expression Data: A Survey","volume":"18","author":"Jiang","year":"2004","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/bioinformatics\/btk026","article-title":"A Multi-Step Approach to Time Series Analysis and Gene Expression Clusterings","volume":"22","author":"Amato","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_14","unstructured":"Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. (2019). Missing Value Estimation Methods for Dna Microarrays. Bioinformatics, in press."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"research0055.1","DOI":"10.1186\/gb-2001-2-12-research0055","article-title":"Evaluation of Normalization Procedures for Oligonucleotide Array Data Based on Spiked cRNA Contros","volume":"2","author":"Hill","year":"2001","journal-title":"Genome Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/28.10.e47","article-title":"Normalization Strategies for cDNA Microarrays","volume":"28","author":"Schuchhardt","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17375","DOI":"10.1007\/s11042-015-3002-x","article-title":"Compressive sampling and adaptive dictionary learning for the packet loss recovery in audio multimedia streaming","volume":"75","author":"Ciaramella","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1049\/iet-com.2014.0995","article-title":"CPacket loss recovery in audio multimedia streaming by using compressive sensing","volume":"10","author":"Ciaramella","year":"2016","journal-title":"IET Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1109\/TCYB.2018.2817480","article-title":"Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification","volume":"45","author":"Li","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1200\/JCO.2004.12.133","article-title":"Microarray gene expression profiling of B-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and VH mutation status","volume":"22","author":"Haslinger","year":"2004","journal-title":"J. Clin. Oncol."},{"key":"ref_22","first-page":"1602","article-title":"Gene expression-based classification of malignant gliomas correlates better with survival than histological classification","volume":"63","author":"Nutt","year":"2003","journal-title":"Cancer Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13790","DOI":"10.1073\/pnas.191502998","article-title":"Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses","volume":"98","author":"Bhattacharjee","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"ref_26","first-page":"7388","article-title":"Molecular classification of human carcinomas by use of gene expression signatures","volume":"61","author":"Su","year":"2001","journal-title":"Cancer Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1016\/j.patcog.2005.02.019","article-title":"Pattern Recognition Techniques for the Emerging Field of Bioinformatics: A review","volume":"38","author":"Liew","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C., Keller, J., Krisnapuram, R., and Pal, N.R. (1999). Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer Academic Publisher.","DOI":"10.1007\/b106267"},{"key":"ref_29","unstructured":"McQueen, J.B. (1966, January 7). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0952-7915(99)00074-6","article-title":"Analysis of Large-Scale Gene Expression Data","volume":"12","author":"Sherlock","year":"2000","journal-title":"Curr. Opin. Immunol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1093\/bioinformatics\/18.5.735","article-title":"Adaptive Quality-Based Clustering of Gene Expression Profiles","volume":"18","author":"Smet","year":"2002","journal-title":"Bioinformatics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1101\/gr.9.11.1106","article-title":"Exploring Expression Data: Identification and Analysis of Coexpressed Genes","volume":"9","author":"Heyer","year":"1999","journal-title":"Genome Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1101\/gr.9.11.1093","article-title":"Large-Scale Clustering of cDNA-Fingerprinting Data","volume":"9","author":"Muller","year":"1999","journal-title":"Genome Res."},{"key":"ref_34","unstructured":"Dubes, R., and Jain, A. (1988). Algorithms for Clustering Data, Prentice Hall."},{"key":"ref_35","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, John Wiley & Sons Inc.. [2nd ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kaufman, L., and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley and Sons.","DOI":"10.1002\/9780470316801"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","article-title":"Cluster Analysis and Display of Genome-Wide Expression Patterns","volume":"95","author":"Eisen","year":"1998","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1126\/science.283.5398.83","article-title":"The Transcriptional Program in the Response of Human Fibroblasts to Serum","volume":"283","author":"Iyer","year":"1999","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9212","DOI":"10.1073\/pnas.96.16.9212","article-title":"Distinctive Gene Expression Patterns in Human Mammary Epithelial Cells and Breast Cancers","volume":"96","author":"Perou","year":"1999","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.patrec.2007.01.009","article-title":"Dynamic agglomerative clustering of gene expression proles","volume":"28","author":"Liang","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1073\/pnas.96.6.2907","article-title":"Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation","volume":"96","author":"Tamayo","year":"1999","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1145\/331499.331504","article-title":"Data Clustering: A Review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. Surv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1093\/comjnl\/41.8.578","article-title":"How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis","volume":"41","author":"Fraley","year":"1998","journal-title":"Comput. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1093\/bioinformatics\/18.3.413","article-title":"A Mixture Model-Based Approach to the Clustering of Microarray Expression Data","volume":"18","author":"McLachlan","year":"2002","journal-title":"Bioinformatics"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"McLachlan, G.J., and Peel, D. (2000). Finite Mixture Models, John Wiley & Sons, Inc.","DOI":"10.1002\/0471721182"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1093\/bioinformatics\/17.10.977","article-title":"Model-Based Clustering and Data Transformations for Gene Expression Data","volume":"17","author":"Yeung","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum-Likelihood from Incomplete Data Via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (1995). Self Organizing Maps, Springer.","DOI":"10.1007\/978-3-642-97610-0"},{"key":"ref_49","unstructured":"Shamir, R., and Sharan, R. (2000, January 19\u201323). Click: A Clustering Algorithm for Gene Expression Analysis. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, La Jolla\/San Diego, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1089\/106652799318274","article-title":"Clustering Gene Expression Patterns","volume":"6","author":"Shamir","year":"1999","journal-title":"J. Comput. Biol."},{"key":"ref_51","unstructured":"Jiang, D., Pei, J., and Zhang, A. (2003, January 12). DHC: A Density-Based Hierarchical Clustering Method for Time-Series Gene Expression Data. Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, Bethesda, MD, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ciaramella, A., Staiano, A., Tagliaferri, R., and Longo, G. (2005). NEC: A Hierarchical Agglomerative Clustering based on Fischer and Negentropy Information. Neural Nets, Springer.","DOI":"10.1007\/11731177_8"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ijar.2007.03.013","article-title":"Clustering and visualization approaches for human cell cycle gene expression data analysis","volume":"47","author":"Napolitano","year":"2008","journal-title":"Int. J. Approx. Reason."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.neunet.2007.12.026","article-title":"Interactive data analysis and clustering of genomic data","volume":"21","author":"Ciaramella","year":"2008","journal-title":"Neural Netw."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Camastra, F., Ciaramella, A., Son, L.H., Riccio, A., and Staiano, A. (2019). Fuzzy Similarity-Based Hierarchical Clustering for Atmospheric Pollutants Prediction, Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-030-12544-8_10"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.inffus.2008.11.006","article-title":"Gene Interaction\u2014An evolutionary biclustering approach","volume":"10","author":"Mitra","year":"2009","journal-title":"Inf. Fusion"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.jbi.2015.06.028","article-title":"Biclustering on expression data: A review","volume":"57","author":"Pontes","year":"2015","journal-title":"J. Biomed. Informat."},{"key":"ref_58","unstructured":"Staiano, A., and Tagliaferri, R. (August, January 31). Visualization of High Dimensional Scientific Data, Book of Tutorials. Proceedings of the International Joint Conference on Neural Networks, Montreal, QC, Canada."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic principal component analysis","volume":"21","author":"Tipping","year":"1999","journal-title":"J. R. Stat. Soc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1162\/089976699300016728","article-title":"Mixtures of probabilistic principal component analyzers","volume":"11","author":"Tipping","year":"1999","journal-title":"Neural Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning\u2014Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3233\/IDA-1999-3203","article-title":"SOM-Based Data Visualization Methods","volume":"3","author":"Vesanto","year":"1999","journal-title":"Intell. Data Anal. J."},{"key":"ref_64","unstructured":"Kaski, S. (1997). Data Exploration Using Self Organizing Maps. [Ph.D. Thesis, Helsinki Institute of Technology]."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1162\/089976698300017953","article-title":"GTM: The Generative Topographic Mapping","volume":"10","author":"Bishop","year":"1998","journal-title":"Neural Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/34.667885","article-title":"A hierarchical latent variable model for data visualization","volume":"20","author":"Bishop","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/34.1000238","article-title":"Hierarchical GTM: Constructing localized nonlinear projection manifolds in a principled way","volume":"24","author":"Tino","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jordan, M.I. (1999). Latent variable models. Learning in Graphical Models, MIT Press.","DOI":"10.1007\/978-94-011-5014-9"},{"key":"ref_69","unstructured":"Chang, K. (2000). Nonlinear Dimensionality Reduction Using Probabilistic Principal Surfaces. [Ph.D. Thesis, The University of Texas at Austin]."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1091\/mbc.02-02-0030","article-title":"Identification of genes periodically expressed in the human cell cycle and their expression in tumors","volume":"13","author":"Whitfield","year":"2002","journal-title":"Mol. Biol. Cell"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1091\/mbc.9.12.3273","article-title":"Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization","volume":"9","author":"Spellman","year":"1998","journal-title":"Mol. Biol. Cell"},{"key":"ref_72","unstructured":"Domingos, P. (2015). The Master Algorithms. How the Quest for the Ultimate Learning Machine Will Remake Our World, Hachette Book Group. Basic Books."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ins.2015.08.029","article-title":"Intrinsic dimension estimation: Advances and open problems","volume":"328","author":"Camastra","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat. Biotechnol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: Large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/6\/123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:19Z","timestamp":1760187559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/6\/123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,18]]},"references-count":75,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["a12060123"],"URL":"https:\/\/doi.org\/10.3390\/a12060123","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,18]]}}}