Prediction of piRNA and Disease Association based on Graph Neural Network

Le résumé fourni par la source

Piwi interacting RNA (piRNA) is a type of small non coding RNA with a length of 24-32 nucleotides, mainly expressed in germ cells. Its abnormal expression is closely related to various diseases such as cancer and neurodegenerative diseases. Although biological experiments are the gold standard for identifying the association between piRNA and disease (PDA), their high cost and long cycle limit research progress. Therefore, computational models have become an important tool for assisting in predicting PDA. However, existing computational methods generally suffer from issues such as insufficient feature extraction and imbalanced data. This article proposes a prediction model based on the fusion of graph convolutional network and attention mechanism - RandGCN. This model combines piRNA sequence embedding, heterogeneous graph construction based on random walks, multi-layer graph convolution feature extraction, and multi head attention mechanism with gating units, effectively improving the accuracy and robustness of PDA prediction. The experimental results on the MNDR dataset show that RandGCN performs well in both AUC and AUPR values, demonstrating excellent predictive performance and potential applications.

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