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CFMDA: collaborative filtering-based MiRNA-disease association prediction

机译：CFMDA：基于协作过滤的MiRNA-疾病关联预测

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摘要

MicroRNAs (miRNAs) are increasingly becoming the focus in a number of researches because abundant studies certify miRNAs play vital roles and have critical functions in various biologic processes. Considering the high cost of experiment research to miRNA-disease association, we explore the way to predict the miRNA-disease association using the extensive collaborative filtering in order to diagnose the diseases better. Specifically, we introduce the prediction model of collaborative filtering-based miRNA-disease association prediction (CFMDA) and verify the model by leave-one-out cross validation(LOOCV) and case validation. The CFMDA considers the miRNA functional similarity and disease similarity while uses minimal amount of related information. CFMDA achieves AUCs of 0.8364 using leave-one-out cross validation, which is the highest AUCs compared to other 5 methods. Meanwhile, we obtain more than 85% confirmation of predicted associations using three kinds of case validations. Generally, our method is faster and more effective than other state-of-the-art methods while it doesn't need any negative samples.
机译：MicroRNA（miRNA）越来越成为许多研究的焦点，因为大量研究证明miRNA在各种生物学过程中起着至关重要的作用，并具有关键的功能。考虑到miRNA-疾病关联实验研究的高昂成本，我们探索了使用广泛的协同过滤预测miRNA-疾病关联的方法，以便更好地诊断疾病。具体来说，我们介绍了基于协作过滤的miRNA-疾病关联预测（CFMDA）的预测模型，并通过留一法交叉验证（LOOCV）和案例验证来验证该模型。 CFMDA考虑了miRNA的功能相似性和疾病相似性，同时使用了最少的相关信息。 CFMDA使用留一法交叉验证实现了0.8364的AUC，这是与其他5种方法相比最高的AUC。同时，我们使用三种案例验证获得了超过85％的预测关联确认。通常，我们的方法比其他最新方法更快，更有效，同时不需要任何阴性样品。

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