DCSGMDA:基于堆叠式深度学习协作梯度分解的双通道卷积模型,用于预测 miRNA 与疾病的关联性

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-09-04 DOI:10.1016/j.compbiolchem.2024.108201
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引用次数: 0

摘要

大量研究表明,微小核糖核酸(miRNA)作为重要的生物标志物,在人类疾病中发挥着关键作用。其异常表达往往伴随着特定疾病的出现。因此,研究 miRNA 与疾病的关系可以加深对疾病发病机制的认识,把握疾病的发生和发展过程,促进特定疾病的药物研究。然而,miRNA 与疾病之间仍存在许多未被发现的关系,极大地限制了 miRNA 与疾病相关性的研究。为了探索更多潜在的相关性,我们提出了一种基于堆叠深度学习协作梯度分解的双通道卷积模型来预测miRNA与疾病的关联(DCSGMDA)。首先,我们构建了 miRNA 与疾病的相似性网络以及关联关系网络。其次,利用堆叠深度学习和梯度分解网络以及双通道卷积神经网络充分挖掘潜在特征。最后,通过多层感知器对相关性进行评分。我们使用基于人类微RNA疾病数据库(HMDD)的两个数据集对DCSGMDA进行了5倍和10倍交叉验证实验。此外,还进行了参数实验、消融实验、比较实验以及案例研究。实验结果表明,DCSGMDA 在预测 miRNA 与疾病的关联方面表现良好。
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DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations

Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and diseases can deepen the insights of their pathogenesis, grasp the process of disease onset and development, and promote drug research of specific diseases. However, many undiscovered relationships between miRNAs and diseases remain, significantly limiting research on miRNA-disease correlations. To explore more potential correlations, we propose a dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations (DCSGMDA). Firstly, we constructed similarity networks for miRNAs and diseases, as well as an association relationship network. Secondly, potential features were fully mined using stacked deep learning and gradient decomposition networks, along with dual-channel convolutional neural networks. Finally, correlations were scored by a multilayer perceptron. We performed 5-fold and 10-fold cross-validation experiments on DCSGMDA using two datasets based on the Human MicroRNA Disease Database (HMDD). Additionally, parametric, ablation, and comparative experiments, along with case studies, were conducted. The experimental results demonstrate that DCSGMDA performs well in predicting miRNA-disease associations.

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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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