Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.

IF 4.4 1区 生物学 Q1 BIOLOGY
Li Peng, Wang Wang, Zongyi Yang, Xiangzheng Fu, Wei Liang, Dongsheng Cao
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引用次数: 0

Abstract

Background: Circular RNAs (circRNAs) and microRNAs (miRNAs) interactions have essential implications in various biological processes and diseases. Computational science approaches have emerged as powerful tools for studying and predicting these intricate molecular interactions, garnering considerable attention. Current methods face two significant limitations: the lack of precise interpretable models and insufficient representation of homogeneous and heterogeneous molecules.

Results: We propose a novel method, MFERL, that addresses both limitations through multi-scale representation learning and an explainable fine-grained model for predicting circRNA-miRNA interactions (CMI). MFERL learns multi-scale representations by aggregating homogeneous node features and interacting with heterogeneous node features, as well as through novel dual-convolution attention mechanisms and contrastive learning to enhance features.

Conclusions: We utilize a manifold-based method to examine model performance in detail, revealing that MFERL exhibits robust generalization, robustness, and interpretability. Extensive experiments show that MFERL outperforms state-of-the-art models and offers a promising direction for understanding CMI intrinsic mechanisms.

利用可解释的多尺度特征进行细粒度circRNA-miRNA相互作用预测。
背景:环状rna (circRNAs)和微rna (miRNAs)的相互作用在各种生物过程和疾病中具有重要意义。计算科学方法已经成为研究和预测这些复杂分子相互作用的强大工具,引起了相当大的关注。目前的方法面临两个显著的限制:缺乏精确的可解释模型和均质和非均质分子的不足表示。结果:我们提出了一种新的方法,MFERL,它通过多尺度表示学习和可解释的细粒度模型来预测circRNA-miRNA相互作用(CMI),从而解决了两者的局限性。MFERL通过聚合同质节点特征并与异质节点特征交互来学习多尺度表征,并通过新颖的双卷积注意机制和对比学习来增强特征。结论:我们使用基于流形的方法来详细检查模型性能,揭示MFERL具有强大的泛化,鲁棒性和可解释性。大量的实验表明,MFERL优于最先进的模型,为理解CMI的内在机制提供了一个有希望的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Biology
BMC Biology 生物-生物学
CiteScore
7.80
自引率
1.90%
发文量
260
审稿时长
3 months
期刊介绍: BMC Biology is a broad scope journal covering all areas of biology. Our content includes research articles, new methods and tools. BMC Biology also publishes reviews, Q&A, and commentaries.
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