Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations
Abstract Background miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and compl...
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BMC
2025-01-01
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| Online Access: | https://doi.org/10.1186/s12863-024-01293-z |
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| author | Kai-Cheng Chuang Ping-Sung Cheng Yu-Hung Tsai Meng-Hsiun Tsai |
| author_facet | Kai-Cheng Chuang Ping-Sung Cheng Yu-Hung Tsai Meng-Hsiun Tsai |
| author_sort | Kai-Cheng Chuang |
| collection | DOAJ |
| description | Abstract Background miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and complicated to analyze by current bioinformatics tools. In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data. Hence, developing a model to predict, identify, and rank connections in miRNAs and diseases can significantly enhance the precision and efficiency in investigating the relationships between miRNAs and diseases. Results In this study, we utilized miRNA-disease association data obtained by biotechnological experiments to develop a DL model for miRNA-disease associations. To improve the accuracy of prediction in this model, we introduced two labeling strategies, weight-based and majority-based definitions, to classify miRNA-disease associations. After preprocessing, data was trained with a novel model combining gated recurrent units (GRU) and graph convolutional network (GCN) to predict the level of miRNA-disease associations. The miRNA-disease association datasets were from HMDD (the Human miRNA Disease Database) and categorized by two distinct labeling approaches, weight-based definitions and majority-based definitions. We classified the miRNA-disease associations into three groups, “upregulated”, “downregulated” and “nonspecific”, by regression analysis and multiclass classification. This GRU-GCN coordinated model achieved a robust area under the curve (AUC) score of 0.8 in all datasets, demonstrating the efficacy in predicting potential miRNA-disease relationships. Conclusions By introducing innovative label-preprocessing methods, this study addressed the relationships between miRNAs and diseases, and improved the ambiguity of the results in different experiments. Based on these refined label definitions, we developed a DL-based model to refine and predict the results of associations between miRNAs and diseases. This model offers a valuable tool for complementing traditional experimental methods and enhancing our understanding of miRNA-related disease mechanisms. |
| format | Article |
| id | doaj-art-e17ae6370555429890e279a951c32fee |
| institution | Kabale University |
| issn | 2730-6844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomic Data |
| spelling | doaj-art-e17ae6370555429890e279a951c32fee2025-01-19T12:40:31ZengBMCBMC Genomic Data2730-68442025-01-0126111110.1186/s12863-024-01293-zEstablishing a GRU-GCN coordination-based prediction model for miRNA-disease associationsKai-Cheng Chuang0Ping-Sung Cheng1Yu-Hung Tsai2Meng-Hsiun Tsai3Department of Life Sciences, National Chung Hsing UniversityDepartment of Management Information Systems, National Chung Hsing UniversityDepartment of Management Information Systems, National Chung Hsing UniversityDepartment of Management Information Systems, National Chung Hsing UniversityAbstract Background miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and complicated to analyze by current bioinformatics tools. In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data. Hence, developing a model to predict, identify, and rank connections in miRNAs and diseases can significantly enhance the precision and efficiency in investigating the relationships between miRNAs and diseases. Results In this study, we utilized miRNA-disease association data obtained by biotechnological experiments to develop a DL model for miRNA-disease associations. To improve the accuracy of prediction in this model, we introduced two labeling strategies, weight-based and majority-based definitions, to classify miRNA-disease associations. After preprocessing, data was trained with a novel model combining gated recurrent units (GRU) and graph convolutional network (GCN) to predict the level of miRNA-disease associations. The miRNA-disease association datasets were from HMDD (the Human miRNA Disease Database) and categorized by two distinct labeling approaches, weight-based definitions and majority-based definitions. We classified the miRNA-disease associations into three groups, “upregulated”, “downregulated” and “nonspecific”, by regression analysis and multiclass classification. This GRU-GCN coordinated model achieved a robust area under the curve (AUC) score of 0.8 in all datasets, demonstrating the efficacy in predicting potential miRNA-disease relationships. Conclusions By introducing innovative label-preprocessing methods, this study addressed the relationships between miRNAs and diseases, and improved the ambiguity of the results in different experiments. Based on these refined label definitions, we developed a DL-based model to refine and predict the results of associations between miRNAs and diseases. This model offers a valuable tool for complementing traditional experimental methods and enhancing our understanding of miRNA-related disease mechanisms.https://doi.org/10.1186/s12863-024-01293-zmiRNAsGRU (gated recurrent unit)GCN (graph convolutional network)miRNA-disease assosications |
| spellingShingle | Kai-Cheng Chuang Ping-Sung Cheng Yu-Hung Tsai Meng-Hsiun Tsai Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations BMC Genomic Data miRNAs GRU (gated recurrent unit) GCN (graph convolutional network) miRNA-disease assosications |
| title | Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations |
| title_full | Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations |
| title_fullStr | Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations |
| title_full_unstemmed | Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations |
| title_short | Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations |
| title_sort | establishing a gru gcn coordination based prediction model for mirna disease associations |
| topic | miRNAs GRU (gated recurrent unit) GCN (graph convolutional network) miRNA-disease assosications |
| url | https://doi.org/10.1186/s12863-024-01293-z |
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