Revising the language of multi-omics AI: A framework for clinically transparent cancer detection

Abstract

Cancer's clinical manifestation is preceded by a cascade of molecular disruptions across multiple biological scales, from genomic instability to metabolic dysregulation. While genomics, transcriptomics, proteomics, epigenomics, and metabolomics have each provided valuable insights, analyses confined to a single molecular layer offer an incomplete and often misleading view of the disease. The advent of Artificial Intelligence (AI) presents a compelling opportunity to synthesize these diverse data streams. However, many AI-driven multi-omics pipelines remain black boxes, prioritizing predictive accuracy over biological transparency and clinical utility. This review critically examines the state of AI-based multi-omics integration and introduces a conceptual countermeasure: the Biology-Informed Explainable Multi-Omics Intelligence Framework (Bio-EMIF). Bio-EMIF moves beyond the typical "data in, prediction out" paradigm by embedding biological knowledge, rigorous uncertainty quantification, and multi-level explain ability as foundational design principles. The framework is structured as a translational pipeline, proceeding from quality-controlled data acquisition and biological prior encoding through multimodal representation learning and graph-based modeling, culminating in a decision-support system that provides not just a risk score, but a biological rationale for its prediction. This approach is particularly critical for early cancer detection, where subtle signals are easily overwhelmed by noise, biological heterogeneity, and technical artifacts. We argue that the field must shift its focus from accumulating more data and increasing model complexity to building systems that are biologically grounded, externally validated, and inherently interpretable for clinicians. Bio-EMIF serves as a conceptual roadmap for this essential evolution in precision oncology.