PDF(Pubmed)
Abstract：
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.
摘要：
脊髓神经系统的损伤通常会导致永久性的感觉丧失，电机,和自主功能。准确识别脊髓神经的细胞状态极为重要，可以促进新的治疗和康复策略的开发。用于鉴定脊髓神经发育的现有实验技术是劳动密集型且昂贵的。在这项研究中,我们开发了一个机器学习预测器，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在测试数据集上达到94.08%94.24%，94.26%,精度为94.24%，召回，和F1测量分数，分别。重要的是,ScnML通过模型解释和生物景观分析确定了新的重要基因。ScnML可以成为预测脊髓神经元细胞状态的强大工具，快速有效地揭示潜在的特定生物标志物，并为精准医学和康复康复提供重要见解。
