Advancing Precision Oncology through Multi-Omics Integration and Explainable Artificial Intelligence Frameworks for Personalized Cancer Therapeutics
Keywords:
Precision Oncology, Multi-Omics Integration, Explainable Artificial Intelligence, Clinical Decision Support Systems, Socio-Technical Infrastructure, Algorithmic Fairness.Abstract
The realization of precision oncology requires the seamless translation of heterogeneous, high-dimensional biomedical data into actionable clinical decisions. While multi-omics integration—encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics—offers an unprecedented, holistic view of tumor biology, its clinical utility is fundamentally bottlenecked by the analytical limitations of traditional computational paradigms. Deep learning models have demonstrated remarkable success in extracting complex, non-linear relationships from these vast datasets, yet their black-box nature represents a significant barrier to clinical adoption. Medical practitioners cannot ethically or legally act upon predictions generated by uninterpretable algorithms, particularly when selecting highly toxic or experimental personalized therapeutic regimens.
This paper presents a comprehensive computational and socio-technical framework that bridges the gap between advanced multi-omics integration and explainable artificial intelligence (XAI). We evaluate the structural architectures required to ingest and harmonize disparate biomolecular streams, detailing the systemic trade-offs between early, late, and intermediate fusion techniques. Furthermore, we dissect the integration of post-hoc explainability methods, such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, alongside inherently interpretable self-explaining architectures like attention-based transformers and capsule networks. Beyond the purely computational domain, we address the critical socio-technical infrastructure necessary for clinical deployment, focusing on data governance, semantic interoperability, cloud-edge hybrid computing topologies, algorithmic fairness across diverse populations, and regulatory validation pathways. By framing precision oncology as a complex, socio-technical system, this research outlines a robust, scalable, and transparent blueprint for the next generation of intelligent clinical decision
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