基于分层注意模型的哺乳动物piRNA靶标预测。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Tianjiao Zhang, Liang Chen, Haibin Zhu, Garry Wong
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引用次数: 0

摘要

背景:piwi相互作用rna (piRNAs)在生殖细胞中监测和保护基因组免受转座子的侵害已经得到了很好的证实。近年来,大量研究证明pirna在调控mRNA转录水平方面也发挥着重要作用。尽管它们在调节细胞RNA水平方面发挥着重要作用,但piRNA的靶向规则尚未明确,特别是在哺乳动物中,这给阐明piRNA的功能带来了障碍。结果:考虑到哺乳动物piRNA靶向规则的复杂性和目前的局限性,我们基于先前实验推断的piRNA靶向模式,通过选择合适的深度学习子网络,设计了一个深度学习模型。此外,为了缓解数据不足的问题,采用了迁移学习方法。我们的模型在预测独立测试数据集时取得了良好的判别能力(准确率:98.5%)。最后,利用该模型预测piRNA数据库中所有小鼠和人类piRNA的靶标。结论:在本研究中,我们开发了一个深度学习框架,该框架显著推进了piRNA靶标的预测,克服了数据不足和当前不完整的靶向规则所带来的限制。piRNA目标预测网络和结果可从https://github.com/SofiaTianjiaoZhang/piRNATarget下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mammalian piRNA target prediction using a hierarchical attention model.

Background: Piwi-interacting RNAs (piRNAs) are well established for monitoring and protecting the genome from transposons in germline cells. Recently, numerous studies provided evidence that piRNAs also play important roles in regulating mRNA transcript levels. Despite their significant role in regulating cellular RNA levels, the piRNA targeting rules are not well defined, especially in mammals, which poses obstacles to the elucidation of piRNA function.

Results: Given the complexity and current limitation in understanding the mammalian piRNA targeting rules, we designed a deep learning model by selecting appropriate deep learning sub-networks based on the targeting patterns of piRNA inferred from previous experiments. Additionally, to alleviate the problem of insufficient data, a transfer learning approach was employed. Our model achieves a good discriminatory power (Accuracy: 98.5%) in predicting an independent test dataset. Finally, this model was utilized to predict the targets of all mouse and human piRNAs available in the piRNA database.

Conclusions: In this research, we developed a deep learning framework that significantly advances the prediction of piRNA targets, overcoming the limitations posed by insufficient data and current incomplete targeting rules. The piRNA target prediction network and results can be downloaded from https://github.com/SofiaTianjiaoZhang/piRNATarget .

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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