Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments. In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy. FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases. In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.

Increasing evidence has indicated that microRNAs (miRNAs) regulate gene expression at the post-transcriptional level. Aberrant miRNA expression has been associated with many types of human disease, including cancers. Their associations can be used to understand the pathogenesis of diseases. However, using experimental methods to identify the associations between diseases and miRNAs is time consuming and costly. Computational methods could find the most promising miRNA-disease associations in a short time, thereby significantly reducing experimental time and cost. This paper presents a network similarity integration method (NSIM) for predicting potential miRNA-disease associations, considering that diseases associated with highly related miRNAs are more similar (and vice versa). The NSIM is based on 5425 experimentally verified human miRNA-disease associations, which consist of 495 miRNAs and 381 diseases. The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict novel miRNA-disease associations. We evaluate the NSIM using leave-one-out cross validation. The area under the curve of the method is 0.9475, indicating its outstanding performance. Case studies on prostate, breast, and colon neoplasms further proved the outstanding performance of the NSIM to predict not only disease-related miRNAs but also isolated diseases (diseases without any related miRNAs).

Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP). The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.