Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning.
Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning.
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DOI：
10.1016/j.fsigen.2022.102722
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Kang Ren;Liangliang Wang;Yifei Wang;G. An;Q. Du;Jie Cao;Q. Jin;Keming Yun;Zhongyuan Guo-Zhongyuan
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文献类型：
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作者：
Kang Ren;Liangliang Wang;Yifei Wang;G. An;Q. Du;Jie Cao;Q. Jin;Keming Yun;Zhongyuan Guo-Zhongyuan
准确估计伤口年龄是调查故意伤害案的关键。建立客观可靠的生物指标来判断伤口年龄仍然是法医学面临的重大挑战。因此，探索一种客观、灵活、可靠的基于下一代测序基因表达谱的伤口年龄评估指标体系选择方法是十分必要的。我们将63只sd大鼠随机分为对照组、7个实验组（每组7只）和外部验证组。实验组和外验证组大鼠挫伤后，分别于挫伤后4、8、12、16、20、24、48 h处死。我们选择了54个在相邻时间点之间变化最显著的基因，并定义了集合a。通过下一代测序和生物信息学分析，具有时间相关表达模式的Hub基因被定义为集合B、C和D。采用逻辑回归、支持向量机、多层感知器和随机森林四种不同的机器学习分类算法，比较并验证了四种指标体系对伤口年龄估计的效率。创伤年龄估计的最佳组合是集合A的基因与随机森林分类算法相结合。外部验证的准确度为85.71%。只有1只大鼠被错误分类（损伤后4小时被错误分类为8小时）。该研究显示了基于下一代测序和生物信息学分析的指标系统选择在伤口年龄估计中的潜在优势。
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.