biostudies-literatureUnknown13(Suppl 2)Li X<h4>Background</h4>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.<h4>Methods</h4>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 scoBMC systems biology26https://www.ebi.ac.uk/biostudies/studies/S-EPMC6449885biostudies-literatureFCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.PMC6449885Li XYang JGu CLin YfalseFCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.<h4>Background</h4>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.<h4>Methods</h4>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 sco2019-01-01T00:00:00Z2019 Apr2025-04-18T20:25:52.895Z2019-06-06T21:06:44ZS-EPMC64498853095351210.1186/s12918-019-0696-9