Data fusion-based algorithm for predicting miRNA-Disease associations
Copyright © 2020 Elsevier Ltd. All rights reserved..
Technological progress and the development of laboratory techniques and bioinformatics tools have led to the availability of ever-increasing amounts of biological data including genomic, proteomic, and transcriptomic sequences and related information. These data have helped in understanding some of the complicated life process from a systematic level. Many diseases are generated by abnormalities in multiple regulating processes. In this study, we constructed a novel miRNA-gene-disease fusion (MGDF) algorithm by integrating three genome-wide networks, namely microRNA (miRNA), gene function, and disease similarity networks. The data fusion method was applied to construct a miRNA-gene-disease association network model from these networks to explore miRNA-disease associations mediated by genes with similar functions. mmiRNAs bind to their target genes and regulate their expression, so the miRNA-gene and gene-disease regulatory relationships were included in the network model to more accurately predict miRNA-disease associations. The proposed MGDF was used to predict miRNA-cancer associations and the results show that most of the predicted associations had evidence in existing databases.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:88 |
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| Enthalten in: |
Computational biology and chemistry - 88(2020) vom: 02. Okt., Seite 107357 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Chunyu [VerfasserIn] |
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| Links: |
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| Themen: |
Disease |
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| Anmerkungen: |
Date Completed 20.05.2021 Date Revised 20.05.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiolchem.2020.107357 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM314093567 |
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| 520 | |a Copyright © 2020 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Technological progress and the development of laboratory techniques and bioinformatics tools have led to the availability of ever-increasing amounts of biological data including genomic, proteomic, and transcriptomic sequences and related information. These data have helped in understanding some of the complicated life process from a systematic level. Many diseases are generated by abnormalities in multiple regulating processes. In this study, we constructed a novel miRNA-gene-disease fusion (MGDF) algorithm by integrating three genome-wide networks, namely microRNA (miRNA), gene function, and disease similarity networks. The data fusion method was applied to construct a miRNA-gene-disease association network model from these networks to explore miRNA-disease associations mediated by genes with similar functions. mmiRNAs bind to their target genes and regulate their expression, so the miRNA-gene and gene-disease regulatory relationships were included in the network model to more accurately predict miRNA-disease associations. The proposed MGDF was used to predict miRNA-cancer associations and the results show that most of the predicted associations had evidence in existing databases | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Network fusion | |
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| 700 | 1 | |a Sun, Kai |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Juexin |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Maozu |e verfasserin |4 aut | |
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