Liu Lijia, Huang Yuxuan, Zheng Yuan, Liao Yihan, Ma Siyuan, Wang Qian
School of Recreation and Community Sport, Capital University of Physical Education and Sports, Beijing, China.
Department of Neuroscience in the Behavioral Sciences, Duke University and Duke Kunshan University, Suzhou, Jiangsu, China.
Front Genet. 2024 Jun 4;15:1413484. doi: 10.3389/fgene.2024.1413484. eCollection 2024.
Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery.
脊髓神经系统损伤常导致感觉、运动和自主功能的永久性丧失。准确识别脊髓神经的细胞状态极其重要，有助于开发新的治疗和康复策略。现有的用于识别脊髓神经发育的实验技术既耗费人力又成本高昂。在本研究中，我们开发了一种机器学习预测器ScnML，用于预测脊髓神经细胞亚群以及识别标记基因。在训练数据集上评估了ScnML的预测性能，准确率为94.33%。基于XGBoost，ScnML在测试数据集上的准确率分别为94.08%、94.24%、94.26%和94.24%，精确率、召回率和F1值分数也分别达到相应水平。重要的是，ScnML通过模型解释和生物学景观分析识别出了新的重要基因。ScnML可以成为预测脊髓神经元细胞状态、快速有效地揭示潜在特异性生物标志物以及为精准医学和康复恢复提供关键见解的有力工具。
