Data-Driven Strategies for Efficient Study Planning in High-Dimensional Healthcare Omics and Biomarker Discovery

Recent advancements in systems biology have been propelled by the increasing power of omics technologies—spanning genomics, transcriptomics, proteomics, metabolomics, and innovations in single-cell and spatial profiling. These tools allow unprecedented resolution in characterizing biological systems, yet translating high-dimensional data into actionable insights remains a challenge. Key obstacles include the high cost and complexity of multi-omics studies, which force researchers to make critical design decisions about what, when, and where to measure that directly affect the reliability and interpretability of results. Traditional approaches based on exhaustive data collection are rarely feasible due to limited budgets, biological material, batch effects, and data missingness. As a result, there is a growing need for principled computational methods that can guide efficient experimental design and optimize resource allocation while maintaining data quality and biological relevance. Although several computational techniques—such as active learning, Bayesian experimental design, and adaptive sampling—have shown promise in related domains, integrating these approaches into multi-omics study planning is still in its early stages. Current evidence suggests that thoughtfully designed studies not only enable robust statistical inference but also facilitate generalizability and translational impact, yet systematic benchmarks and widely supported frameworks are lacking.This Research Topic aims to advance the computational foundations and practical methodologies for optimizing multi-omics study design. We seek to explore and promote algorithms and computational workflows that strategically maximize information gain, reproducibility, and translational readiness in omics research. Our goal is to foster innovation in cost-aware and constraint-aware experimental planning, power and sample size estimation for high-dimensional data, adaptive design for time-series and perturbation studies, and integrative methods that leverage both data-driven and mechanistic models. Important questions include: How can study designs be tailored to specific biological, clinical, or translational objectives? What algorithmic strategies best address constraints on budget, sample size, and missingness? How can benchmarks be established to compare approaches and drive adoption of new methodologies?The scope of this Research Topic covers computational strategies to optimize the design and planning of multi-omics experiments, including both methodological advances and applied case studies. We encourage submissions of algorithms, reproducible pipelines, theoretical analyses, and carefully constructed benchmarks, as well as real-world studies demonstrating the impact of design-aware computational methods.To gather further insights in this domain, we welcome articles addressing, but not limited to, the following themes:o Active learning and adaptive sampling applied to multi-omicso Bayesian and optimal decision-making frameworks for experimental designo Cost-aware and constraint-aware assay selection strategieso Power and sample size estimation for high-dimensional multi-omics datao Experimental planning for time-resolved and perturbation studieso Integrative approaches combining mechanistic/network models with data-driven methodso Benchmarks and pipelines for reproducible multi-omics study planningo Demonstration case studies of improved inference and translational outcomes using optimized study designs. Topic editors: Nuriye Sancar, Erol Eğrioğlu, Ufuk Beyaztas Source: https://www.frontiersin.org/research-topics/78618/data-driven-strategies-for-efficient-study-planning-in-high-dimensional-healthcare-omics-and-biomarker-discovery