Faculty of Engineering, University of Jaffna, Sri Lanka.
Pathol Res Pract. 2023 Feb;242:154311. doi: 10.1016/j.prp.2023.154311. Epub 2023 Jan 15.
SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.
SARS-CoV-2 大流行是当前世界面临的巨大威胁，导致了大量的疾病。由于大多数国家的资源有限，特别是在重症监护和氧气方面，因此准确预测疾病的严重程度至关重要。这种预测将有助于医疗界选择需要这些有限资源的患者。文献表明，在这项研究中使用临床数据是一种常见趋势，而很少利用分子数据进行这种预测。由于分子数据携带更多与疾病相关的信息，因此在这项研究中，我们使用了三种不同类型的 SARS-CoV-2 患者的 RNA 分子（lncRNA、miRNA 和 mRNA）来预测这些患者的严重程度阶段和治疗阶段。使用七种不同的机器学习算法和几种特征选择技术表明，在两种表型中，特征重要性选择的特征与随机森林分类器一起提供了最佳的准确性。此外，它还表明，在严重程度阶段预测中，miRNA 和 lncRNA 的表现最佳，而 lncRNA 数据在治疗阶段预测中表现最佳。由于大多数与分子数据相关的研究都使用 mRNA 数据，因此这是一个有趣的发现。
