Computational Methods for the Analysis of Genomic Data and Biological Processes

Dear Colleagues,

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms.

As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency.

The aim of this Special Issue is to collect the latest advances in the field of computational methods for the analysis of gene expression data, and in particular with the modelling of biological processes.

We encourage researchers to share their original works in the field of computational analysis of gene expression data. Topics of primary interest include, but are not limited to:

1. Computational methods or machine learning approaches for modelling biological processes;
2. Discovering genome–disease or genome–phenotype associations;
3. Gene–gene interactions and gene–environment interactions for disease association analysis;
4. New computational methods for gene expression data analysis;
5. Machine learning approaches for modelling gene regulatory networks;
6. Identification of expression patterns;
7. Reviews on computational methods for gene expression data analysis.

Update: A 2nd edition is being edited here https://www.mdpi.com/journal/genes/special_issues/comput_genetics_II

Prof. Dr. Francisco A. Gómez Vela
Prof. Dr. Federico Divina
Prof. Dr. Miguel García Torres
Guest Editors

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