In the era of precision medicine, the intersection between AI and genetics holds transformative potential, could solve longstanding hurdles in genomic interpretation and biomarker-driven therapy. The sheer volume and multifaceted nature of multi-omics data often overwhelm conventional genetic frameworks, which remain ill-equipped to decode intricate disease networks, frequently stretching manual interpretation across grueling months. Current breakthroughs demonstrate AI's capacity to reshape the field. Like, these studies include the Predictive Biomarker Modeling Framework (PBMF) has streamlined the discovery of actionable markers, effectively de-risking the high-stakes process of drug development (1); An machine learning (ML) approach utilizes million-scale electronic health records to quantify pathogenic probabilities of over 1,600 genetic variants, replacing rigid binary classifications with continuous risk scores and overcoming biases from small-cohort analyses (2). This Research Topic, "Genetic Horizons: Exploring Genetic Biomarkers in Therapy and Evolution with the aid of Artificial Intelligence", aims to explore three core questions: how to leverage AI to overcome traditional genetic analysis limitations; how to address the gap of inefficient algorithms for seamless integration of laboratory and clinical data; and how to validate genomic algorithms via multicenter studies to enhance biomarker accuracy and biological relevance. This editorial summarizes key findings from featured articles, offering novel insights into genetic mechanisms through diverse experimental approaches.Rather than struggling with the 'curse of dimensionality,' ML leverages advanced architectures to parse vast mutation and expression arrays that are simply too intricate This editorial summarizes that AI-genetics integration, driven by ML algorithms, effectively overcomes traditional genetic analysis' high-dimensional data processing bottlenecks. The five studies focus on disease-specific biomarker screening, multi-dimensional data integration (public datasets + clinical samples, multi-omics), and precision medicine advancement, exemplified by high-accuracy diagnostic models for ATAAD (AUC=0.935) and IgA nephropathy (AUC=0.942), and pathogenic mutation identification in DMD. Despite the significant strides documented here, several systemic hurdles must be cleared before these tools can be fully integrated into clinical practice. A primary obstacle remains the friction between disparate data types, although Xie et al. (2024) made a commendable preliminary attempt at multi-omics integration, the current lack of specialized algorithms continues to hinder the continuous, real-time synchronization required for clinical workflows (5). Furthermore, the robustness of these findings is frequently constrained by a lack of large-scale, multicenter validation, as seen in the small cohorts of Cui et al.'s (2025) osteoarthritis study which may limit the generalizability of the results (4).Beyond data volume, the "interpretability gap" remains a persistent concern: the "black-box" nature of many high-performing models means that even when core genes are identified as demonstrated in the ATAAD study by Pan et al. (2025), the specific mechanistic links to immune infiltration remain frustratingly opaque (3).Finally, the applicability of these AI-driven genetic frameworks remains underexplored in rare diseases and diverse global populations, a gap that must be bridged to ensure equitable clinical translation. These gaps collectively hinder reliable clinical translation of AI-driven genetic research.These studies collectively push forward AI's application in genetic biomarker research, laying a foundation for precision medicine's clinical translation. We invite readers from diverse perspectives to continue the conversation and share feedback to address remaining challenges.
Wu et al. (Fri,) studied this question.