Machine Learning Applications in Biology—2nd Edition

Dear Colleagues,

Gene regulatory networks (GRNs) represent a fundamental mechanism for maintaining the homeostasis of cells, while their inherent plasticity enables the occurrence of dynamic processes such as cell differentiation and adaptation to environmental stimuli, among others. Abrupt changes in GRNs, which can often be attributed to environmental factors or genetic variation, often lead to the development of pathological conditions, including cancer, autoimmune disorders, etc.

GRN is an umbrella term that refers to the complex set of interactions between genomic and epigenomic elements that drive the fine-tuning process of gene expression. GRNs typically consist of elements such as protein-coding and noncoding RNAs (i.e., long noncoding RNAs and microRNAs), transcriptional (i.e., DNA binding proteins) and post-transcriptional (i.e., RNA binding proteins and RNA modification enzymes) regulators, chromatin remodeling factors, DNA methylation enzymes and virtually any type of molecule that is implicated in the mechanisms affecting gene expression.

Machine learning (ML) has been an indispensable tool at the hands of researchers studying any of the aforementioned elements. From building ML-based computational methods for, e.g., predicting transcription factor binding sites, genomic loci that harbor genes, microRNA–gene interactions, RNA binding protein recognition sites and histone-modification-driven genome segmentation to modeling complex relationships between the environment and genetic variation or integrating multipurpose experimental data, ML has been instrumental in shedding light on the darkest corners of biology research.

This Special Issue aims to be the substrate of disseminating state-of-the-art and high-quality research regarding ML applications on any of the aforementioned fields, since we believe that these fields represent the quintessence of biology research and perfectly fit the aim and scope of this journal. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Genomics;
• Epigenomics;
• Interplay between genetic variation and the environment;
• Gene regulatory networks;
• Integration of multipurpose next-generation sequencing data (bulk or single cell) in the context of the aforementioned thematic areas;
• Epidemiology.

I look forward to receiving your contributions.

Dr. Georgios K. Georgakilas
Guest Editor

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• machine learning
• genomics
• epigenomics
• genetic variation and the environment
• gene regulatory networks
• integration of multipurpose NGS data