Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-07-07 , DOI: 10.1007/s12539-021-00458-z Li Zhang 1, 2, 3 , Pengyu Yang 4 , Huawei Feng 1 , Qi Zhao 5 , Hongsheng Liu 2, 3, 6
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LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA–miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA.
Graphic Abstract
中文翻译：
使用网络距离分析预测 lncRNA-miRNA 相互作用
LncRNA-miRNA 相互作用有助于调节多种人类疾病的治疗靶点和诊断生物标志物。然而，大规模地通过实验鉴定 lncRNA-miRNA 关联仍然很困难，而且计算预测方法也很有限。在这项研究中，我们开发了一个用于 lncRNA-miRNA 关联预测（NDALMA）的网络距离分析模型。计算 lncRNA 和 miRNA 的相似性网络，并将其与高斯相互作用图谱 (GIP) 核相似性相结合。然后，将网络距离分析应用于集成的相似性网络，经过置信度计算和分数转换后得到最终分数。我们的模型在五重交叉验证中获得了令人满意的结果，AUC 为 0.8810，AUPR 为 0.8315。而且，NDALMA 显示出优于其他几种网络算法的预测性能，我们通过比较不同类型的相似性来测试模型的适用性和灵活性。此外，还进行了 lncRNA 和 miRNA 之间关系的案例研究，这验证了我们的方法在预测 lncRNA-miRNA 关联方面的可靠性。本研究中使用的数据集和源代码可在 https://github.com/Liu-Lab-Lnu/NDALMA 获得。
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