Explainable Deep Learning Frameworks for Predicting Transplant Compatibility from High-Resolution Immune Gene Haplotypes Derived from Long-Read Data
Keywords:
explainable artificial intelligence, transplant compatibility, HLA typing, long-read sequencing, deep learning, clinical decision support, algorithmic fairness, governanceAbstract
The accurate prediction of transplant compatibility remains a critical challenge in clinical medicine, particularly given the extreme polymorphism of human leukocyte antigen (HLA) genes and the increasing availability of long-read sequencing data that can resolve full-length haplotypes with unprecedented resolution. Deep learning models offer powerful capabilities for capturing complex, non-linear interactions among immune gene variants, yet their opaque nature raises significant concerns for clinical deployment where interpretability, trust, and regulatory compliance are paramount. This paper presents a comprehensive systems-level analysis of explainable deep learning frameworks designed to predict transplant outcomes from high-resolution immune gene haplotypes derived from long-read data. We examine the architectural trade-offs inherent in integrating long-read sequencing pipelines with deep neural networks, emphasizing the need for modular, scalable, and auditable system designs. The discussion encompasses model transparency techniques such as attention mechanisms, Shapley additive explanations, and concept-based interpretability methods, and evaluates their suitability for graft survival prediction, acute rejection risk stratification, and donor-recipient matching. Beyond technical considerations, we address governance, fairness, and policy implications, including data privacy for genomic information, algorithmic bias across ethnic populations, and the regulatory pathways required for clinical adoption. Cross-domain comparisons with analogous challenges in precision oncology and drug discovery are drawn to contextualize the infrastructure requirements and sustainability challenges of deploying such systems at scale. By synthesizing technical, ethical, and operational dimensions, we provide a forward-looking framework for building robust, equitable, and explainable AI systems that can transform transplant medicine while upholding the highest standards of accountability and safety.
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