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Background: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease., Methods: In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations., Results: The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases., Conclusions: These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations-based miRNA-disease association (DRMDA) prediction. The original miRNA-disease association data were extracted from HDMM database. Meanwhile, stacked auto-encoder, greedy layer-wise unsupervised pre-training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA-disease associations. [ABSTRACT FROM AUTHOR]

Prediction of unobserved microRNA (miRNA)-disease associations is one of the most important research fields due to miRNA's roles of diagnostic biomarkers and therapeutic targets for large number of human complex diseases. Thus, the development of effective computational methods for identification of novel miRNA-disease associations would provide a unique opportunity to design better therapeutic interventions. In this study, we presented a novel computational model named Matrix Decomposition and Label Propagation for MiRNA-Disease Association prediction (MDLPMDA) by integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. Based on the new adjacency matrix of miRNA-disease associations obtained from matrix decomposition through sparse learning method, the model is presented by implementing label propagation process on the constructed integrated miRNA similarity network and integrated disease similarity network, respectively, and then using an average ensemble strategy to combine the two different prediction models. At last, AUCs of 0.9222 and 0.8490 in global and local leave-one-out cross-validation (LOOCV) proved the model's reliable performance. In addition, AUC of 0.9211+/-0.0004 in 5-fold cross-validation confirmed its accuracy and stability. We further implemented case studies to predict potential miRNAs associated with human complex diseases based on different versions of HMDD database. We also carried out case studies on diseases without any known related miRNAs to examine the prediction performance of MDLPMDA. At last, the analysis of the assessment results of cross validations and case studies indicated that MDLPMDA could be an effective method to infer novel miRNA-disease associations. [ABSTRACT FROM AUTHOR]