Bridging the Credibility Gap: Robust Validation and Explainable AI for Omics-Based Biomarkers

The field of omics-based biomarker discovery has generated a vast array of gene signatures, molecular network modules, and pathway biomarkers, propelled by advances in high-throughput technologies and analytical frameworks. However, despite the promise of these data-driven discoveries, a persistent “clinical credibility gap” remains: most proposed biomarkers fail to demonstrate the essential qualities of reproducibility, transportability, calibration, and interpretability required for clinical adoption. Recent studies have made significant strides in identifying network biomarkers using approaches such as WGCNA, differential expression, and LASSO-based selection. Yet, few go beyond initial discovery to meet robust multi-cohort validation standards, address domain shifts, or provide mechanistic biological explanations. This gap continues to hinder the translation of exploratory omics findings into decision-ready tools for patient stratification, prognosis, and therapy selection.This Research Topic aims to accelerate the transformation of exploratory omics signatures into network-level biomarkers that are systematically validated and clinically actionable. We seek to encourage methodological rigor by integrating classic network bioinformatics with emerging AI and machine learning (ML) paradigms. We are especially interested in research that explicitly addresses the generalizability of biomarker performance across diverse cohorts and technical platforms, employing designs such as internal-external validation and independent replication of network properties. Equally important are approaches that leverage Foundation Models, Graph Neural Networks (GNNs), and Causal AI to foster explainability through biologically contextualized and mechanistically grounded models.The scope encompasses work that advances decision-ready biomarkers by linking rigorous validation with interpretability and actionable insight. Submissions should focus on the development and evaluation of network-, module-, or pathway-based biomarkers derived from multi-omics data (bulk, single-cell, spatial, and longitudinal). We welcome articles addressing, but not limited to:- Multi-cohort and multicenter validation of network and AI-derived biomarkers.- Application of Foundation Models and Transfer Learning for cross-platform and cross-population biomarker discovery.- Methods for calibration, clinical utility analysis, and uncertainty quantification.- Geometric Deep Learning and Graph-based architectures for capturing complex molecular interactions.- Strategies for domain shift, batch effect, and harmonization in multi-omics research.- Explainability (XAI) and mechanistic integration of biological networks, pathways, and causal inference.- Integration of prior biological knowledge and graph-based learning in biomarker discovery.- Benchmarking and open workflows for reproducible and robust omics development.- Robustness of model explanations to retraining, perturbations, and knowledge biases.- Comparative medicine and veterinary studies applying network biomarker discovery, multi-omics validation, and AI/ML frameworks in translational or clinically relevant contexts. Topic editors: Claire Yik-Lok Chung, Qin CAO, Ruby Srivastava Source: https://www.frontiersin.org/research-topics/79629/bridging-the-credibility-gap-robust-validation-and-explainable-ai-for-omics-based-biomarkers