Systems Biology Approaches to Genetic Data of Human Diseases
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Genetics and Genomics".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 174
Special Issue Editor
Interests: system biology; computational biology; statistical genetics; Alzheimer's disease; type-2 diabetes; cardio vascular diseases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advances in high-throughput genomics have transformed our ability to study the biological mechanisms underlying human diseases. Yet, the complexity of disease pathways demands analytical frameworks that extend beyond traditional single-gene or single-variant perspectives. Systems biology provides a framework by integrating diverse layers of biological information to uncover emergent properties that cannot be captured through reductionist approaches alone.
This Special Issue focuses on systems biology approaches to genetic data of human diseases, highlighting methods that combine genomic, transcriptomic, epigenomic, proteomic, and/or metabolic information to build a deeper understanding of disease mechanisms. We welcome scientific contributions using network analysis, multi-omics integration, machine learning, predictive systems approaches, and studies exploring gene–gene and gene–environment interactions. By bringing together methodological advances and applications across diverse disease contexts, this Special Issue aims to showcase how systems-level thinking can accelerate discoveries, refine disease classification, and ultimately support the development of more precise diagnostic and therapeutic strategies.
We are pleased to invite you and look forward to receiving your valuable contribution that will push the boundaries of integrative and translational genomics.
I look forward to receiving your contributions.
Dr. Marcio A. A. Almeida
Guest Editor
Manuscript Submission Information
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Keywords
- systems biology
- multi-omics integration
- human genetics
- network analysis
- disease mechanisms
- machine learning
- functional genomics
- gene–environment interactions
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