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PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features
Cheng Yan1,2, Guihua Duan2, Na Li2
1School of Information Science and Engineering, Hunan University of Chinese Medicine, Changsha 410208, China.
Motivation:
Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types.
Results:
In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations.
Availability And Implementation:
Data and source codes are available at https://github.com/27167199/PDMDA.
Insights
This study introduces PDMDA, a deep learning model for predicting deep-level microRNA (miRNA)-disease associations. PDMDA accurately identifies association types using graph neural networks and miRNA sequence features, overcoming limitations of existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial in human diseases, but experimental identification of their associations is costly and time-consuming.
- Existing computational methods predict miRNA-disease associations but not their specific types.
- There is a need for advanced computational approaches to predict deep-level miRNA-disease association types.
Purpose of the Study:
- To develop an end-to-end deep learning method, PDMDA, for predicting deep-level miRNA-disease associations.
- To leverage graph neural networks (GNNs) and miRNA sequence features for enhanced prediction accuracy.
- To differentiate and predict various types of miRNA-disease associations beyond simple existence.
Main Methods:
- PDMDA utilizes a fully connected network (FCN) to extract miRNA feature representations from sequence and structural data.
- Disease feature representations are derived from gene networks using a GNN model.
- A multilayer network with fully connected and softmax layers integrates miRNA and disease features for association scoring.
Main Results:
- PDMDA accurately predicts deep-level miRNA-disease associations, as validated by fivefold cross-validation.
- The model achieved high performance using the area under the receiver operating characteristic curve (AUC) metric.
- Experiments were conducted on six types of association samples, demonstrating the model's versatility.
Conclusions:
- PDMDA offers an effective computational approach for predicting deep-level miRNA-disease association types.
- The method provides a valuable tool for understanding the complex relationships between miRNAs and diseases.
- The developed model advances the field of miRNA-disease association prediction beyond binary classification.
