School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
School of Information Science and Engineering, Hunan University, Changsha, 410082, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac058.
Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ShengPengYu/KDFGMDA.
越来越多的生物学证据表明，微小 RNA（miRNA）在探索各种人类疾病（尤其是肿瘤）的发病机制方面发挥着重要作用。挖掘与疾病相关的 miRNA 对于疾病的临床诊断和治疗具有重要意义。与传统的实验方法相比，基于计算的方法具有成本效益高的优点，但也存在明显的局限性，如周期长、规模小。然而，尽管目前基于计算生物学的方法可以准确预测 miRNA 与疾病之间的相关性，但它们无法在精细水平上预测详细的关联信息。我们提出了一种基于知识驱动的方法来进行疾病相关 miRNA 的精细预测（KDFGMDA）。与以前的方法不同，该方法可以精细地预测 miRNA 与疾病之间的明确关联，例如上调、下调或失调。具体来说，KDFGMDA 从大量的实验数据和现有数据集提取三重信息来构建知识图，然后基于知识图训练深度图表示学习模型，以完成精细的预测任务。实验结果表明，KDFGMDA 可以准确地预测 miRNA 与疾病之间的关系，这对医学临床研究和疾病的早期诊断、预防和治疗具有深远的意义。此外，对三种癌症的案例研究、Kaplan-Meier 生存分析和表达差异分析的结果进一步提供了 KDFGMDA 检测潜在候选 miRNA 的有效性和可行性。可用性：我们的工作可以从 https://github.com/ShengPengYu/KDFGMDA 下载。
