微核糖核酸 (miRNA) 在控制人类转录组的各种生物现象中发挥着关键作用。因此，miRNA 表达失调的积累经常在复杂疾病的发生和进展中发挥重要作用。然而，现阶段准确识别失调的miRNA仍面临挑战。最近出现了几种生物信息学工具来预测 miRNA 与疾病之间的关联。尽管如此，现有的参考工具主要识别一般状态下的 miRNA 与疾病的关联，无法精确定位特定疾病状态下失调的 miRNA。此外，在分析 miRNA-疾病关联时，没有研究充分考虑 miRNA-miRNA 相互作用 (MMI)。在这里，我们引入了一种称为 IDMIR 的系统方法，该方法能够在基因表达背景下通过 MMI 网络识别表达失调的 miRNA，其中网络的架构旨在根据特定疾病背景下共享的生物学功能隐式连接 miRNA 。 IDMIR 的优点在于，它通过分析 MMI 的变化，使用基因表达数据来识别失调的 miRNA。我们通过乳腺癌和膀胱尿路上皮癌的数据分析，展示了 IDMIR 方法对失调 miRNA 的出色预测能力。通过比较，IDMIR 可以超越几种现有的 miRNA 疾病关联预测方法。我们相信该方法弥补了预测 miRNA 与疾病关联的缺陷，并可能为疾病的诊断和治疗提供新的见解和可能性。 IDMIR 方法现已作为 CRAN 上的免费 R 包提供 (https://CRAN.R-project.org/package=IDMIR)。© 作者 2024。由牛津大学出版社出版。

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).© The Author(s) 2024. Published by Oxford University Press.