From Omics to Outcomes: Translational Bioinformatics Powered by Foundation Models

Translational bioinformatics lies at the intersection of computational biology, multi-omics, and clinical data science, offering a powerful toolkit for elucidating complex disease mechanisms and informing tailored therapies. In recent years, the proliferation of multi-layered biological data—ranging from genomics and transcriptomics to imaging and electronic health records—has illuminated intricate molecular interactions but has also introduced significant analytic challenges. Fragmented datasets, modality heterogeneity, and variability in patient populations pose significant obstacles to deriving actionable insights and translating molecular benchmarks into patient outcomes. Foundation models, which are large-scale, pre-trained models capable of ingesting and representing vast biomedical datasets, have emerged as promising tools to integrate disparate modalities and address these translational bottlenecks. Recent studies demonstrate that these models—including large language models, multimodal transformers, and graph-based architectures—excel at generating unified representations of biological systems, enabling more accurate predictions of phenotypes, disease subtyping, and treatment response. Leveraging self-supervised learning, transfer learning, and few-shot adaptation, foundation models require less labeled data, facilitate cross-cohort generalization, and foster new avenues for biomarker discovery and personalized medicine. However, the field still faces pressing questions regarding the interpretability of these integrative models, their robustness across diverse populations, reliable uncertainty quantification, and the ethical considerations of clinical deployment. Despite progress, comprehensive frameworks that connect multi-omics data directly to validated clinical outcomes in diverse settings remain limited and require deeper exploration. Questions surrounding interpretability, mechanistic insight, causal reasoning, calibration, uncertainty quantification, fairness across diverse populations, and ethical deployment in clinical environments must be addressed before widespread implementation can be realized. Furthermore, standardized and reproducible frameworks that directly connect integrated multi-omics representations to validated clinical endpoints across real-world settings are still limited. Prospective validation strategies, regulatory considerations, and seamless integration into clinical workflows represent essential next steps for translating foundation models from computational promise to bedside impact.This Research Topic aims to advance integrative bioinformatics by gathering innovative contributions on the development, evaluation, and responsible application of foundation models for the translation of omics data into clinically meaningful endpoints. The objective is to foster end-to-end approaches that seamlessly unify multi-omics, imaging, and clinical data, guiding both mechanistic insights and patient-centric decision-making. Authors are encouraged to address core methodological questions, evaluate model generalizability and fairness, and propose practical pipelines that move the field closer to robust, outcome-driven applications. Submissions emphasizing on prospective validation, regulatory considerations, and integration within real-world clinical workflows will be particularly valued.The scope of the Research Topic spans original research, methods, benchmarks, and perspectives focused on translational applications and model robustness limits. To gather further insights in the integrative bioinformatics of omics-to-outcome translation powered by foundation models, we welcome articles addressing, but not limited to, the following themes:o Methodological advances in multi-omics, imaging, and clinical data integration using foundation modelso Strategies for model interpretability, mechanistic insight, and causal inference with integrated datao Approaches to generalization, external validation, and calibration for clinical adoptiono Digital twin frameworks and patient-specific predictive modelingo Benchmark datasets, evaluation standards, and reproducibility frameworks for biomedical foundation modelso Federated and privacy-preserving foundation models for cross-institutional learningo Bias, fairness, and privacy considerations in modeling patient and population datao Practical pipelines and resources for reproducible, outcome-driven bioinformatics applications. Topic editors: Sahar Qazi Source: https://www.frontiersin.org/research-topics/79150/from-omics-to-outcomes-translational-bioinformatics-powered-by-foundation-models