City Research Online

Abstract

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated protein 9 (Cas9) system has revolutionized and changed the genome editing by enabling precise and efficient genetic modifications across various diverse biological applications. Despite its ability, the broader therapeutic adoption of CRISPR-Cas9, particularly in vivo settings, is constrained drastically by immunogenicity risks arising from guide RNA (gRNA) target interactions and unintended host immune recognition patterns. Accurate prediction of these T-cell epitope reactivity is essential to ensure safety, mitigate adverse immune responses in application, and guide the rational design of genome-editing therapies. In this work, we propose CRISPR-ImmunoPred, a phased hybrid prediction framework that integrates gradient boosting and deep learning for immunogenicity-aware CRISPR design framework. The framework comprises of (i) a Particle Swarm Optimization (PSO) optimized XGBoost based on gRNA Viability Profiler for assessing CRISPR editing fitness and (ii) an Attentive Immunogenicity Inference model which employs cross-attention Transformer architectures to explicitly capture guide target sequence interactions alongside numerical biological features. Comprehensive sensitivity analysis, systematic ablation studies, and group-wise generalization experiments finally demonstrate that each architectural component contributes meaningfully to predictive performance and that the proposed framework degrades gracefully under biologically meaningful distributional shifts. On combining high predictive accuracy with partial interpretability through feature attribution and architectural analysis, CRISPR-ImmunoPred provides a robust and modular approach for balancing editing efficiency and immune safety, supporting safer CRISPR-based therapeutic development and rational genome editing design.

Publication Type:	Article
Additional Information:	© 2026 IEEE. This accepted manuscript is made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	CRISPR-Cas9, XGBoost, particle swarm optimization (PSO), immune response, gene editing, epitope prediction, immunogenicity prediction
Subjects:	H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history > QH426 Genetics R Medicine > RC Internal medicine
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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