AI-Driven Computational Genetics: Integrative Models, Scalable Frameworks, and Translational Impact

The rapid advancement of high-throughput sequencing and multi-omics technologies has transformed genetics into an inherently data-intensive discipline. In parallel, breakthroughs in artificial intelligence (AI), including machine learning and deep learning, have created unprecedented opportunities to uncover complex patterns and derive meaningful insights from large-scale biological data.However, translating these advances into real-world impact remains a significant challenge. Issues such as data heterogeneity, model robustness, reproducibility, and fairness across diverse populations continue to limit the reliability and generalizability of AI-driven discoveries. At the same time, there is a critical need for computational solutions that are not only accurate, but also scalable, deployment-ready, and suitable for integration in clinical and biomedical settings.This Research Topic addresses these challenges by focusing on integrative and scalable AI approaches in computational genetics, with particular emphasis on the fusion of heterogeneous data sources — such as genomic, transcriptomic, epigenomic, and clinical data — to generate actionable biological and clinical insights. It is uniquely positioned to highlight research that bridges methodological innovation with translational impact, bringing together contributions that demonstrate robustness, cross-domain integration, and real-world applicability.We welcome original research articles, reviews, and perspectives in (but not limited to) the following areas:• Multi-omics data integration (genomic, transcriptomic, epigenomic, and clinical)• Cloud-based and parallelized AI workflows for large-scale genetic analysis• Functional genomics and gene regulatory network modeling• Benchmarking and validation of AI models in genomics• Data curation, quality assessment strategies, and standardized evaluation frameworks in AI-driven studies• Model interpretability methods in support of scalable and reproducible biological and clinical workflows• Case studies demonstrating translational deployment of AI models in genomic research or clinical settingsSubmissions that make all associated code openly available are particularly encouraged, as this directly supports the reproducibility and community reuse that this Research Topic aims to promote. Topic editors: Amal Khalifa, Maryam Al-Berry Source: https://www.frontiersin.org/research-topics/81626/ai-driven-computational-genetics-integrative-models-scalable-frameworks-and-translational-impact