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Katherine1216 75274bab6f | 2 years ago | |
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code | 2 years ago | |
MDA-GKNN_requirements.txt | 2 years ago | |
README | 2 years ago | |
README.md | 2 years ago |
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are of costly and time consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GNNFTG, which predicts MDAs based on graph neural networks via graph sampling through the feature and topology graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GNNFTG can not only predict new miRNA-disease associations, but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results show that the MDA-GNNFTG method has achieved satisfactory performance on all six tasks, and significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction method. Moreover, the effectiveness of GNN via the graph samping strategy and the feature and topology graph has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
The implementation of random forest, extremely randomized trees, Gaussian naivy Bayes, decision trees, SVM, graph convolutional network, GAMEDA
It is worthy to note that the DNN is implemented through Keras.
It contains the pytorch code for MDA-GKNN.
The MDA prediction results on six tasks, which contain y_train, y_test, y_train_pred, y_test_pred
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Jupyter Notebook C++ Python Cython
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