Comparison of computational integration methods.
| Method category | Key techniques | Strengths | Limitations | Typical applications | References |
|---|---|---|---|---|---|
| Classical statistical/ML | PCA, clustering, RF, SVM, sCCA | Interpretable; computationally efficient; established theoretical foundations; works well with limited samples | Linear or shallow non-linear; limited cross-omics interaction capture; manual feature engineering required | Disease subtyping; biomarker discovery; baseline benchmarks | [49–51] |
| AE-based | Denoising AEs, VAEs, stacked AEs | Unsupervised feature learning; handles missing data; dimensionality reduction; generative capabilities | Latent space may not be interpretable; requires large samples; computationally intensive | Single-cell integration; missing modality imputation; representation learning | [9] |
| Graph neural networks | GCNs, GATs, geometric GNNs | Incorporates biological priors; captures network structure; intrinsically interpretable via attention | Graph construction critical; scalability challenges; over-smoothing with deep layers | Pathway-informed modeling; patient similarity networks; biomarker discovery | [52–54] |
| Transformer/Attention | Self-attention, cross-modal attention, hierarchical attention | Captures long-range dependencies; intrinsic interpretability; flexible fusion strategies | Quadratic complexity with feature count; large data requirements; attention faithfulness concerns | Cancer classification; survival prediction; multimodal fusion | [55, 56] |
| Fusion strategies | Early, intermediate, late, hierarchical fusion | Balances integration depth and interpretability; modular design | No universal optimal strategy; trade-offs between cross-layer interaction capture and interpretability | All multi-omics tasks; clinical decision support | [13, 47] |
ML: machine learning; PCA: principal component analysis; RF: random forest; SVM: support vector machine; sCCA: sparse canonical correlation analysis; AEs: autoencoders; VAEs: variational autoencoders; GCNs: graph convolutional networks; GATs: graph attention networks.
During the preparation of this work, the authors used Google AI tools to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
MH: Conceptualization, Writing—original draft, Visualization. SH: Writing—review & editing, Supervision. KA: Methodology, Writing—review & editing, Formal analysis. MW: Methodology, Formal analysis, Writing—review & editing. All authors have read and approved the final version of the manuscript.
The authors declare that they have no conflicts of interest.
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No new data were generated or analyzed in this review. All referenced datasets are publicly available from the sources cited.
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