{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(Suppl 2)"],"submitter":["Li X"],"pubmed_abstract":["<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 sco"],"journal":["BMC systems biology"],"pagination":["26"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6449885"],"repository":["biostudies-literature"],"pubmed_title":["FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs."],"pmcid":["PMC6449885"],"pubmed_authors":["Li X","Yang J","Gu C","Lin Y"],"additional_accession":[]},"is_claimable":false,"name":"FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.","description":"<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 sco","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Apr","modification":"2025-04-18T20:25:52.895Z","creation":"2019-06-06T21:06:44Z"},"accession":"S-EPMC6449885","cross_references":{"pubmed":["30953512"],"doi":["10.1186/s12918-019-0696-9"]}}