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<p>PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining genomic stability and inhibiting transposable elements, and have been proven to participate in various diseases by regulating gene expression and influencing signaling pathways. Traditional biological experimental methods have limitations such as low throughput, long cycles, and high costs, making them difficult to meet the requirements of large-scale systematic screening. In this study, we develop a predictive framework named PiDA-DVLSA. We integrate autoencoder, dual graph transformer, and multi-head self-attention mechanisms, and construct an end-to-end multimodal deep learning system. We use autoencoder to perform nonlinear dimensionality reduction and denoising on piRNA sequence features and disease phenotype semantic features, and extract potential representations with strong discriminative ability. Then, we use graph transformers to model the high-order topological relationships between nodes in isomorphic similar graphs, and input heterogeneous graph transformers to learn complex cross-entity interaction patterns in heterogeneous networks. Finally, we achieve adaptive fusion of multi-source information through multi-head self-attention mechanisms. PiDA-DVLSA performs excellently on the benchmark dataset, with AUC and AUPR reach 0.9437 and 0.9195, respectively, significantly outperform eight mainstream algorithms. In independent case validations for breast cancer, clioblastoma, and Alzheimer disease, our model successfully predicts multiple biologically significant potential associations, further confirming its practicality and effectiveness in real scientific research scenarios and providing a solid computational basis for future precision diagnostic and therapeutic applications. PiDA-DVLSA is freely available at <a href="https://github.com/zhaoqi106/PiDA-DVLSA">https://github.com/zhaoqi106/PiDA-DVLSA</a>.</p> Graphical abstract <p></p>

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Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Manyu Zheng,
  • Ying Fang,
  • Lijie Na,
  • Qi Zhao

摘要

PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining genomic stability and inhibiting transposable elements, and have been proven to participate in various diseases by regulating gene expression and influencing signaling pathways. Traditional biological experimental methods have limitations such as low throughput, long cycles, and high costs, making them difficult to meet the requirements of large-scale systematic screening. In this study, we develop a predictive framework named PiDA-DVLSA. We integrate autoencoder, dual graph transformer, and multi-head self-attention mechanisms, and construct an end-to-end multimodal deep learning system. We use autoencoder to perform nonlinear dimensionality reduction and denoising on piRNA sequence features and disease phenotype semantic features, and extract potential representations with strong discriminative ability. Then, we use graph transformers to model the high-order topological relationships between nodes in isomorphic similar graphs, and input heterogeneous graph transformers to learn complex cross-entity interaction patterns in heterogeneous networks. Finally, we achieve adaptive fusion of multi-source information through multi-head self-attention mechanisms. PiDA-DVLSA performs excellently on the benchmark dataset, with AUC and AUPR reach 0.9437 and 0.9195, respectively, significantly outperform eight mainstream algorithms. In independent case validations for breast cancer, clioblastoma, and Alzheimer disease, our model successfully predicts multiple biologically significant potential associations, further confirming its practicality and effectiveness in real scientific research scenarios and providing a solid computational basis for future precision diagnostic and therapeutic applications. PiDA-DVLSA is freely available at https://github.com/zhaoqi106/PiDA-DVLSA.

Graphical abstract