A Digital Twin with Transfer Learning Enables Cross-Anatomical Forecasting of Postmortem Microbiome Dynamics for PMI Estimation

Forensic microbiology leverages postmortem microbiome succession as a promising biomarker for estimating the postmortem interval (PMI). However, current methods are constrained by sparse sampling (typically 3–5 time points) and poor cross-anatomical generalizability, leading to imprecise PMI estimates with errors often exceeding ± 3 days, particularly in cases of dismembered remains. To overcome these, we developed mHolmes, a transformer-based digital twin framework powered by transfer learning. Trained on high-resolution data from 34 cadavers over 21 days, mHolmes achieves accurate daily predictions of microbial dynamics, reducing errors and demonstrating high accuracy (MAE < ± 2 days) in cross-anatomical forecasting (e.g., hip to face). Shapley Additive exPlanations (SHAP) analysis ensures interpretability by identifying seven key bacterial classes as conserved biomarkers. This study highlights mHolmes as a robust, high-precision tool that addresses critical bottlenecks, enabling reliable PMI estimation from incomplete data with significant applications in forensic investigations, such as body part matching and daily-resolution timeline reconstruction.