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Editorial
. 2020 Oct 20;11(10):1230.
doi: 10.3390/genes11101230.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Affiliations
Editorial

Computational Methods for the Analysis of Genomic Data and Biological Processes

Francisco Gómez-Vela et al. Genes (Basel). .

Abstract

Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...].

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Conflict of interest statement

The authors declare no conflict of interest.

References

    1. Vanhaeren T., Divina F., García-Torres M., Gómez-Vela F., Vanhoof W., Martínez-García P.M. A comparative study of supervised machine learning algorithms for the prediction of long-range chromatin interactions. Genes. 2020;11:985. doi: 10.3390/genes11090985. - DOI - PMC - PubMed
    1. Rehman M.U., Chong K.T. DNA6mA-MINT: DNA-6mA modification identification neural tool. Genes. 2020;11:898. doi: 10.3390/genes11080898. - DOI - PMC - PubMed
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    1. Delgado-Chaves F.M., Gómez-Vela F., Divina F., García-Torres M., Rodriguez-Baena D.S. Computational analysis of the global effects of Ly6E in the immune response to coronavirus infection using gene networks. Genes. 2020;11:831. doi: 10.3390/genes11070831. - DOI - PMC - PubMed
    1. Zahoor J., Zafar K. Classification of microarray gene expression data using an infiltration tactics optimization (ITO) algorithm. Genes. 2020;11:819. doi: 10.3390/genes11070819. - DOI - PMC - PubMed

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