Multi-omics data integration in bioinformatics: methods, applications, and the path toward biological reasoning.

High-throughput technologies now make it possible to simultaneously generate genomic, transcriptomic, epigenomic, proteomic, and metabolomic data. Spatial and single-cell platforms, in turn, provide cellular and spatial information in greater detail. Relying on a single omics method captures only a fraction of this complexity and often overlooks interactions across different layers. Despite these advances, clinical applications remain limited. This review examines integration strategies, along with their applications in various diseases, and the machine learning methods used to implement them. Rather than focusing solely on predictive performance, the aim is to support biological reasoning by formulating testable hypotheses, drawing causal inferences, and gaining insights that can guide clinical decisions. A reanalysis of a publicly available chronic lymphocytic leukemia dataset revealed that one data type was missing in approximately one-third of patients. When early and intermediate integration were combined with simple zero imputation, agreement dropped to chance and near-chance levels. In contrast, late integration, which does not require data imputation, was largely unaffected by this issue. This demonstrates that findings regarding integration strategies cannot be considered in isolation from how missing data are handled. The review also covers approaches that directly address missing data. Major barriers to clinical application include confounding batch effects, unreliable feature attribution when features are correlated, and deficiencies in clinical quality assurance. This review proposes four criteria to help identify models that not only ensure statistical validity but also genuinely support biological reasoning: mechanistic specificity, causal grounding, failure transparency, and decision relevance.