siRNADiscovery:通过深度 RNA 序列分析预测 siRNA 药效的图神经网络。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Rongzhuo Long, Ziyu Guo, Da Han, Boxiang Liu, Xudong Yuan, Guangyong Chen, Pheng-Ann Heng, Liang Zhang
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引用次数: 0

摘要

小干扰 RNA(siRNA)的临床应用促使人们开发了各种 siRNA 设计计算策略,从传统的数据分析到先进的机器学习技术。然而,以往的研究没有充分考虑 siRNA 沉默机制的全部复杂性,忽略了 siRNA 在 mRNA 上的定位、RNA 碱基配对概率以及 RNA-AGO2 相互作用等关键因素,从而限制了现有模型的洞察力和准确性。在这里,我们介绍了 siRNADiscovery,这是一种图神经网络(GNN)框架,它利用 siRNA 和 mRNA 的非经验和经验规则特征,有效捕捉基因沉默的复杂动态。在多个内部数据集上,siRNADiscovery 实现了最先进的性能。值得注意的是,siRNADiscovery 在体外研究和外部验证数据集上的表现也优于现有方法。此外,我们还开发了一种新的数据分割方法,解决了以往研究中经常忽视的数据泄露问题,确保了我们的模型在各种实验环境下的鲁棒性和稳定性。通过严格的测试,siRNADiscovery 显示出了非凡的预测准确性和稳健性,为基因沉默领域做出了重大贡献。此外,我们重新定义数据分割标准的方法旨在为 siRNA 预测生物学建模领域的未来研究树立新的标杆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA-AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery, a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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