Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
<b>Background:</b> Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence t...
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MDPI AG
2025-01-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/13/1/136 |
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| author | Jihwan Ha |
| author_facet | Jihwan Ha |
| author_sort | Jihwan Ha |
| collection | DOAJ |
| description | <b>Background:</b> Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the underlying pathogenesis of human diseases. However, identifying miRNA–disease associations using biological experiments is inefficient in terms of cost and time. <b>Methods:</b> Here, we discuss a novel machine-learning model that effectively predicts disease-related miRNAs using a graph convolutional neural network with neural collaborative filtering (GCNCF). By applying the graph convolutional neural network, we could effectively capture important miRNAs and disease feature vectors present in the network while preserving the network structure. By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. <b>Results:</b> Extensive experimental results based on area under the curve (AUC) scores (0.9216 and 0.9018) demonstrated the superiority of our model over previous models. <b>Conclusions:</b> We anticipate that our model could not only serve as an effective tool for predicting disease-related miRNAs but could be employed as a universal computational framework for inferring relationships across biological entities. |
| format | Article |
| id | doaj-art-d6484ca4f5a645cda487e8122a830497 |
| institution | Kabale University |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-d6484ca4f5a645cda487e8122a8304972025-01-24T13:24:08ZengMDPI AGBiomedicines2227-90592025-01-0113113610.3390/biomedicines13010136Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease AssociationJihwan Ha0Major of Big Data Convergence, Division of Data Information Science, Pukyong National University, Busan 48513, Republic of Korea<b>Background:</b> Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the underlying pathogenesis of human diseases. However, identifying miRNA–disease associations using biological experiments is inefficient in terms of cost and time. <b>Methods:</b> Here, we discuss a novel machine-learning model that effectively predicts disease-related miRNAs using a graph convolutional neural network with neural collaborative filtering (GCNCF). By applying the graph convolutional neural network, we could effectively capture important miRNAs and disease feature vectors present in the network while preserving the network structure. By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. <b>Results:</b> Extensive experimental results based on area under the curve (AUC) scores (0.9216 and 0.9018) demonstrated the superiority of our model over previous models. <b>Conclusions:</b> We anticipate that our model could not only serve as an effective tool for predicting disease-related miRNAs but could be employed as a universal computational framework for inferring relationships across biological entities.https://www.mdpi.com/2227-9059/13/1/136graph convolutional networkneural collaborative filteringmiRNAdiseasemachine learning |
| spellingShingle | Jihwan Ha Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association Biomedicines graph convolutional network neural collaborative filtering miRNA disease machine learning |
| title | Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association |
| title_full | Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association |
| title_fullStr | Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association |
| title_full_unstemmed | Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association |
| title_short | Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association |
| title_sort | graph convolutional network with neural collaborative filtering for predicting mirna disease association |
| topic | graph convolutional network neural collaborative filtering miRNA disease machine learning |
| url | https://www.mdpi.com/2227-9059/13/1/136 |
| work_keys_str_mv | AT jihwanha graphconvolutionalnetworkwithneuralcollaborativefilteringforpredictingmirnadiseaseassociation |