About this Research Topic
The field of computational genomics, with a focus on epigenomics, has witnessed remarkable advancements in decoding the complexities of human genetic disorders. Epigenomic data, encompassing DNA methylation, histone modifications, and chromatin accessibility, offer unprecedented insights into the regulatory landscape of the genome. This multidimensional information has the potential to unravel the intricate relationships between epigenetic alterations and the manifestation of various genetic disorders.
This Research Topic aims to address the challenges of decoding human genetic disorders through computational approaches in epigenomics. The increasing volume and complexity of epigenomic data demand sophisticated computational tools for accurate interpretation. Recent advances include the development of machine learning algorithms for the identification of disease-associated epigenetic patterns, integrative analyses to decipher regulatory networks, and the utilization of deep learning models to predict disease susceptibility. However, challenges persist in the refinement of predictive models, cross-platform data integration, and the translation of findings into clinically actionable insights.
Contributors are invited to delve into diverse themes within the scope of computational approaches for deciphering human genetic disorders based on epigenomics. Topics of interest include but are not limited to:
1. Development and optimization of machine learning algorithms for epigenomic data analysis.
2. Integrative analyses combining epigenomic and genomic data to uncover regulatory mechanisms.
3. Application of deep learning models in predicting and understanding genetic disorder susceptibility.
4. Cross-disciplinary approaches bridging computational genomics and clinical translation.
5. Validation studies and efforts to bridge the gap between computational predictions and experimental evidence.
This Research Topic aims to address the challenges of decoding human genetic disorders through computational approaches in epigenomics. The increasing volume and complexity of epigenomic data demand sophisticated computational tools for accurate interpretation. Recent advances include the development of machine learning algorithms for the identification of disease-associated epigenetic patterns, integrative analyses to decipher regulatory networks, and the utilization of deep learning models to predict disease susceptibility. However, challenges persist in the refinement of predictive models, cross-platform data integration, and the translation of findings into clinically actionable insights.
Contributors are invited to delve into diverse themes within the scope of computational approaches for deciphering human genetic disorders based on epigenomics. Topics of interest include but are not limited to:
1. Development and optimization of machine learning algorithms for epigenomic data analysis.
2. Integrative analyses combining epigenomic and genomic data to uncover regulatory mechanisms.
3. Application of deep learning models in predicting and understanding genetic disorder susceptibility.
4. Cross-disciplinary approaches bridging computational genomics and clinical translation.
5. Validation studies and efforts to bridge the gap between computational predictions and experimental evidence.
Keywords: computational approach, genetic disorders, epigenomics, 3D genomics, disease diagnosis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
