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Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering

机译：基于改进的混合协作滤波预测miRNA疾病关联的高效框架

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Accumulating studies indicates that microRNAs (miRNAs) play vital roles in the process of development and progression of many human complex diseases. However, traditional biochemical experimental methods for identifying disease-related miRNAs cost large amount of time, manpower, material and financial resources. In this study, we developed a framework named hybrid collaborative filtering for miRNA-disease association prediction (HCFMDA) by integrating heterogeneous data, e.g., miRNA functional similarity, disease semantic similarity, known miRNA-disease association networks, and Gaussian kernel similarity of miRNAs and diseases. To capture the intrinsic interaction patterns embedded in the sparse association matrix, we prioritized the predictive score by fusing three types of information: similar disease associations, similar miRNA associations, and similar disease-miRNA associations. Meanwhile, singular value decomposition was adopted to reduce the impact of noise and accelerate predictive speed. We then validated HCFMDA with leave-one-out cross-validation (LOOCV) and two types of case studies. In the LOOCV, we achieved 0.8379 of AUC (area under the curve). To evaluate the performance of HCFMDA on real diseases, we further implemented the first type of case validation over three important human diseases: Colon Neoplasms, Esophageal Neoplasms and Prostate Neoplasms. As a result, 44, 46 and 44 out of the top 50 predicted disease-related miRNAs were confirmed by experimental evidence. Moreover, the second type of case validation on Breast Neoplasms indicates that HCFMDA could also be applied to predict potential miRNAs towards those diseases without any known associated miRNA. The satisfactory prediction performance demonstrates that our model could serve as a reliable tool to guide the following research for identifying candidate miRNAs associated with human diseases.
机译：积累研究表明，微大RNA（MIRNA）在许多人类复杂疾病的发展过程中起重要作用。然而，传统的生化实验方法，用于鉴定疾病相关的miRNA花费大量时间，人力，材料和财务资源。在这项研究中，我们通过集成异质数据，例如miRNA功能相似性，疾病语义相似性，已知的miRNA疾病协会网络和MiRNA和高斯核心相似性，开发了一种名为MiRNA-Discument Preciention（HCFMDA）的杂交协作滤波的框架。疾病。为了捕获嵌入在稀疏关联矩阵中的内在交互模式，我们通过融合三种类型的信息优先考虑预测得分：类似的疾病关联，类似的miRNA关联和类似的疾病 - miRNA关联。同时，采用奇异值分解来减少噪声的影响并加速预测速度。然后，我们通过休假交叉验证（LooOCV）和两种类型的案例研究验证了HCFMDA。在LooOCV中，我们达到了0.8379的AUC（曲线下的区域）。为了评估HCFMDA对实际疾病的表现，我们进一步实施了三种重要人类疾病的第一种病例验证：结肠肿瘤，食管肿瘤和前列腺肿瘤。结果，通过实验证据确认了前50个预测疾病相关的miRNA中的44,46和44。此外，乳腺肿瘤的第二种病例验证表明HCFMDA也可以应用于预测潜在的MIRNA，而不具有任何已知的相关miRNA的疾病。令人满意的预测性能表明，我们的模型可以作为可靠的工具，以指导以下研究鉴定与人类疾病相关的候选麦芽瘤的研究。

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