Background With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations. Methodology In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms. Results IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries. Conclusion We built a software named IMPMD which can be freely downloaded from https://github.com/Sunmile/IMPMD.

中文翻译：

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IMPMD：一种预测 miRNA 与疾病之间潜在关联的综合方法

背景随着生物学研究的快速发展，microRNAs（miRNAs）越来越受到全世界的关注。越来越多的生物学研究和科学实验证明，miRNA与大量引起复杂人类疾病的关键生物学过程的发生和发展有关。因此，识别miRNA与疾病之间的关联有助于诊断疾病。尽管一些研究发现 miRNA 与疾病之间存在相当大的关联，但仍有许多关联需要确定。揭示 miRNA 与疾病关联的实验方法既耗时又昂贵。因此，迫切需要有效的计算方法来预测新的关联。方法论 在这项工作中，我们提出了一种预测 miRNA 与疾病之间潜在关联的综合方法 (IMMPD)。miRNAs的增强相似性是通过功能相似性、高斯相似性和Jaccard相似性的组合获得的。对于疾病，通过语义相似度、高斯相似度和Jaccard相似度组合得到。然后，我们使用这两个增强的相似性来构建特征并计算累积分数以选择稳健的特征。最后，应用一般线性回归为支持向量机、K-Nearest Neighbor 和 Logistic 回归算法分配权重。结果 IMPMD 在 10 倍交叉验证中获得了 0.9386 的 AUC，优于之前的大多数模型。为了进一步评估我们的模型，我们在肺癌和乳腺癌的两种案例研究中实施了 IMPMD。前 50 个相关 miRNA 中有 49 个（肺癌）和 50 个（乳腺癌）通过实验发现得到验证。结论 我们构建了一个名为 IMPMD 的软件，可以从 https://github.com/Sunmile/IMMPD 免费下载。