IEEE/ACM Trans Comput Biol Bioinform. 2024 Jan-Feb;21(1):45-56. doi: 10.1109/TCBB.2023.3335007. Epub 2024 Feb 5.
MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation). We evaluated MRFGMDA and found that it accurately predicted miRNA-disease associations, which could have far-reaching implications for clinical medical analysis, early diagnosis, prevention, and treatment. Case analyses, Kaplan-Meier survival analysis, expression difference analysis, and immune infiltration analysis further demonstrated the effectiveness and feasibility of MRFGMDA in uncovering potential disease-related miRNAs. Overall, our work represents a significant step toward improving the prediction of miRNA-disease associations using a fine-grained approach could lead to more accurate diagnosis and treatment of diseases.
微小 RNA（miRNAs）在诊断和治疗各种疾病中起着关键作用。最近，自动揭示 miRNAs 和疾病之间相互依存的关系取得了显著进展，但它们的精细相互关系仍需要探索。我们提出了一种用于 miRNA-疾病关联的细粒度预测的多关系图编码器网络（MRFGMDA），该网络使用实际和当前的数据集构建了一个多关系图编码器网络，以预测与疾病相关的 miRNAs 及其特定的关系类型（上调、下调或失调）。我们评估了 MRFGMDA，发现它可以准确预测 miRNA-疾病关联，这对临床医学分析、早期诊断、预防和治疗具有深远的意义。案例分析、Kaplan-Meier 生存分析、表达差异分析和免疫浸润分析进一步证明了 MRFGMDA 在揭示潜在疾病相关 miRNAs 方面的有效性和可行性。总的来说，我们的工作代表了朝着使用细粒度方法提高 miRNA-疾病关联预测的重要一步，这可能导致更准确的疾病诊断和治疗。
