Scholarlink

<p>MicroRNAs (miRNAs) play a key role in the prevention, diagnosis, and treatment of complex diseases. However, identifying miRNA-disease associations (MDAs) through traditional methods is costly and time-consuming. Recent studies have reported numerous validated MDAs, forming the basis for the prediction of new MDAs using computational methods. In this study, we propose SAETNMDA, a computational method that applies fast kernel learning (FKL) and variant triplet networks to predict MDAs. First, miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data. Next, feature representations are obtained by applying stacked autoencoders (SAEs) and triplet networks, enabling the identification of associated pairs by mapping them to nearby locations in the embedding space, while unassociated ones are mapped distantly. Finally, we utilize XGBoost (Extreme Gradient Boosting) to obtain predictive scores for MDAs from these features. SAETNMDA’s performance is evaluated with 5-fold cross-validation (5-fold-CV) and compared with other methods. It achieves the highest AUC and AUPR (0.941 9, 0.474 9 for HMDD v2.0; 0.949 6, 0.535 5 for HMDD v3.2, respectively). The performance is also validated on an independent dataset and <i>de novo</i> miRNAs, with SAETNMDA achieving the highest AUC and AUPR in all validations. Case studies also demonstrate the robust predictive capability of our method, with the top 50 predicted miRNAs validated for each of the three diseases. These results highlight SAETNMDA as an efficient model for MDA prediction. SAETNMDA’s source code is available at <a href="https://github.com/npxquynhdhsp/SAETNMDA">https://github.com/npxquynhdhsp/SAETNMDA</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MiRNA-Disease Association Prediction Based on Stacked Autoencoders and Variant Triplet Networks

  • 期刊论文

  • 发表日期：

    2025年7月24日

查看全文
  • Nguyen-Phuc-Xuan Quynh,
  • Hoai-Nhan Tran,
  • Cheng Yan,
  • Jian-Xin Wang

摘要

MicroRNAs (miRNAs) play a key role in the prevention, diagnosis, and treatment of complex diseases. However, identifying miRNA-disease associations (MDAs) through traditional methods is costly and time-consuming. Recent studies have reported numerous validated MDAs, forming the basis for the prediction of new MDAs using computational methods. In this study, we propose SAETNMDA, a computational method that applies fast kernel learning (FKL) and variant triplet networks to predict MDAs. First, miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data. Next, feature representations are obtained by applying stacked autoencoders (SAEs) and triplet networks, enabling the identification of associated pairs by mapping them to nearby locations in the embedding space, while unassociated ones are mapped distantly. Finally, we utilize XGBoost (Extreme Gradient Boosting) to obtain predictive scores for MDAs from these features. SAETNMDA’s performance is evaluated with 5-fold cross-validation (5-fold-CV) and compared with other methods. It achieves the highest AUC and AUPR (0.941 9, 0.474 9 for HMDD v2.0; 0.949 6, 0.535 5 for HMDD v3.2, respectively). The performance is also validated on an independent dataset and de novo miRNAs, with SAETNMDA achieving the highest AUC and AUPR in all validations. Case studies also demonstrate the robust predictive capability of our method, with the top 50 predicted miRNAs validated for each of the three diseases. These results highlight SAETNMDA as an efficient model for MDA prediction. SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.