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Descripción
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| MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Predicting potential miRNAdisease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNAdisease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA target association and disease miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. | |
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Internacional
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JCR del ISI
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Título de la revista
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Ieee/Acm Transactions on Computational Biology And Bioinformatics |
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ISSN
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1545-5963 |
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Factor de impacto JCR
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Información de impacto
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Volumen
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DOI
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10.1109/TCBB.2017.2776280 |
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Número de revista
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