Jabeer Amhar, Temiz Mustafa, Bakir-Gungor Burcu, Yousef Malik
Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey.
Department of Information Systems, Zefat Academic College, Zefat, Israel.
Front Genet. 2023 Jan 12;13:1076554. doi: 10.3389/fgene.2022.1076554. eCollection 2022.
During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.
近年来，生物学实验和越来越多的证据表明，微小RNA在人类复杂疾病的诊断和治疗中发挥着重要作用。因此，为了诊断和治疗人类复杂疾病，有必要揭示特定疾病与相关微小RNA之间的关联。尽管当前基于机器学习的计算模型试图确定微小RNA与疾病的关联，但这些模型的准确性仍需提高，并且需要从生物学角度评估候选的微小RNA与疾病的关系。在本文中，我们提出了一种名为miRdisNET的计算模型来预测潜在的微小RNA与疾病的关联。具体而言，miRdisNET需要两种类型的数据，即微小RNA表达谱和已知的疾病与微小RNA的关联作为输入文件。首先，我们通过应用分组组件生成特定疾病的子集。这些子集包含与每个特定疾病相关的带有类别标签的微小RNA表达。然后，我们使用一种用于分类的机器学习方法为每个组分配一个重要性得分。最后，我们应用一个建模组件并获得输出。miRdisNET最重要的输出之一是微小RNA与疾病预测的性能。与现有方法相比，miRdisNET获得了最高的AUC值0.9998。miRdisNET的另一个输出是所研究疾病的显著微小RNA列表。miRdisNET识别出的微小RNA通过参考持有关于经实验验证的微小RNA与疾病关联信息的金标准数据库进行验证。miRdisNET已被开发用于预测尚未知晓微小RNA与疾病关系的新疾病的候选微小RNA。此外，miRdisNET基于共享的微小RNA知识呈现候选的疾病与疾病关联。miRdisNET工具和其他补充文件可在以下网址公开获取：https://github.com/malikyousef/miRdisNET。
