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<p>Understanding microRNA/isomiR–mRNA interactions has long been a major challenge. Although many computational methods exist for predicting miRNA–mRNA interactions, almost all fail to account for isomiR–mRNA interactions. To bridge this gap, we developed misoTar, a fine-tuned BERT-based deep learning model trained on over 6.660&#xa0;million positive and negative microRNA/isomiR–mRNA interaction pairs from 67 publicly available human samples across six studies. In five-fold cross-validation, misoTar achieved an average precision of 0.930 and a recall of 0.898. On independent test datasets, it consistently delivered superior or comparable performance relative to existing tools, including TargetScan, Mimosa, DMISO, and TEC-miTarget. Furthermore, single-nucleotide mutation analysis of true positive interactions highlighted the critical functional importance of non-seed regions in microRNA/isomiR–mRNA targeting. Overall, misoTar offers a robust and accurate framework for predicting microRNA/isomiR–mRNA interactions and provides new insights into microRNA biology. The misoTar tool is available at <a href="https://figshare.com/projects/misoTar/262723">https://figshare.com/projects/misoTar/262723</a>.</p>

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misoTar: a novel approach for predicting miRNA and isomiR targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Rony Chowdhury Ripan,
  • Xiaoman Li,
  • Haiyan Hu

摘要

Understanding microRNA/isomiR–mRNA interactions has long been a major challenge. Although many computational methods exist for predicting miRNA–mRNA interactions, almost all fail to account for isomiR–mRNA interactions. To bridge this gap, we developed misoTar, a fine-tuned BERT-based deep learning model trained on over 6.660 million positive and negative microRNA/isomiR–mRNA interaction pairs from 67 publicly available human samples across six studies. In five-fold cross-validation, misoTar achieved an average precision of 0.930 and a recall of 0.898. On independent test datasets, it consistently delivered superior or comparable performance relative to existing tools, including TargetScan, Mimosa, DMISO, and TEC-miTarget. Furthermore, single-nucleotide mutation analysis of true positive interactions highlighted the critical functional importance of non-seed regions in microRNA/isomiR–mRNA targeting. Overall, misoTar offers a robust and accurate framework for predicting microRNA/isomiR–mRNA interactions and provides new insights into microRNA biology. The misoTar tool is available at https://figshare.com/projects/misoTar/262723.