Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning

Abstract

Accurate estimation of the wound age is critical in investigating intentional injury cases. Establishing objective and reliable biological indicators to estimate wound age is still a significant challenge in forensic medicine. Therefore, exploring an objective, flexible, and reliable index system selection method for wound age estimation based on next-generation sequencing gene expression profiles is necessary. We randomly divided 63 Sprague-Dawley rats into a control group, seven experimental groups (n = 7 per group), and an external validation group. After rats in the experimental and external validation groups suffered contusions, we sacrificed them at 4, 8, 12, 16, 20, 24, and 48 h after contusion, respectively. We selected 54 genes with the most significant changes between adjacent time points after contusion and defined set A. The Hub genes with time-related expression patterns were set B, C, and D through next-generation sequencing and bioinformatics analysis. Four different machine learning classification algorithms, including logistic regression, support vector machine, multi-layer perceptron, and random forest were used to compare and verify the efficiency of four index systems to estimate the wound age. The best combination for wound age estimation is the Genes ascribed to set A combined with the random forest classification algorithm. The accuracy of external verification was 85.71%. Only one rat was incorrectly classified (4 h post-injury incorrectly classified as 8 h). This study demonstrated the potential advantage of the index system selection based on next-generation sequencing and bioinformatics analysis for wound age estimation.