Application of Whole-Genome Regulatory Networks in Studying Drug Action
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 3231
Special Issue Editor
Special Issue Information
Dear Colleagues,
We would like to invite you to participate in this Special Issue, “Application of Whole-Genome Regulatory Networks in Studying Drug Action”.
The burgeoning of next-generation sequencing, systems biology and artificial intelligence in the past few years had provided an unprecedentedly detailed understanding of how gene regulatory networks under specific disease contexts at the genomic scale can affect the action and efficacy of drugs. Such genetic complexity is beyond the conventional drug discovery approach of solely maximizing the binding specificity of drugs to their respective targets but requires broader considerations to include the activities of regulatory networks within disease cells to enhance drug actions. State-of-the-art computational tool development including artificial intelligence and novel systems biology perspectives are needed to revolutionize “next-generation” drug discovery pipelines. The advancement of approaches to prioritize drugs and their respective targets under specific gene regulatory contexts can facilitate the translation of individualized and precision medicine.
The purpose of this Special Issue is to host research, perspectives, and review papers to further advance our understanding to better maximize drug efficacies and minimize side effects by incorporating new knowledge regarding how disease genes and the activity of drug targets are regulated genome-wide. New results, confirmatory results, and contradictory results will also be considered for publication.
Dr. Hu Li
Guest Editor
Manuscript Submission Information
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Keywords
- systems biology
- systems pharmacology
- network biology
- disease network
- gene regulatory network
- reverse engineering
- machine learning
- artificial intelligence
- precision medicine
- individualized medicine
- drug discovery
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