CL-GIE v4.0: Blockchain-Anchored Cross-Layer Genomic Integrity With Hierarchical Bloom–Merkle Trees for Ethics-Aware, Explainable Personalized Gene Editing

Personalized gene editing demands verifiable integrity, governance-aware decisioning, and forensics-grade auditability across heterogeneous evidence sources (CRISPR logs, phenotype outcomes, and ledger records). We presentCL-GIE v4.0, a cross-layer genomic integrity evaluator that fuses: (i) a Hierarchical Bloom–Merkle Tree (HBMT) forO(log N) membership proofs and vertical consistency; (ii) an entropy–KL drift channel that detects distributional shifts across temporal, phenotypic, and edit-type features; (iii) lightweight structural checks (timestamp monotonicity, hash replay); and (iv) an interpretable ethics policy engine. HBMT yields succinct proofs (depth ≈ 14 on our test set) with verified roots, enabling forensics-ready attestations and chain anchoring. CL-GIE operates under tight latency budgets: per-record HBMT verification is ∼0.02 ms, while meta-model scoring remains sub-millisecond, sustaining real-time pipelines. Empirically, CL-GIE surpasses one-class, reconstruction, clustering, and rules baselines. A stacked variant attains state-of-the-art ROC (AUC= 0.991), while isotonic meta-calibration maximizes area under the precision–recall curve (AP = 0.852), reflecting robustness at low prevalence. Confusion matrices corroborate high recall and specificity (e.g., stacked: TP = 480, FN = 23, TN = 9398, FP = 99), aligning with clinical detection priorities and low false-positive exposure. An ablation-driven weight sweep shows that ethics signals and cross-layer drift are pivotal at the optimal operating point, with validation F1≈0.81–0.88 across calibrated and stacked settings. Radar and correlation analyses further indicate balanced gains across AUROC, AUPR, F1, specificity, and calibration metrics. Overall, CL-GIE delivers a principled, explainable, and audit-capable integrity layer for ethically governed gene-editing workflows, unifying cryptographic proofs, drift-aware detection, and policy transparency under a single evaluative framework.