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CBIL-VHPLI:一种基于机器学习和转移学习的模型,用于预测病毒-宿主蛋白-lncRNA相互作用

Man Zhang¹, Li Zhang^1,2,3, Ting Liu^1,4

¹School of Life Science, Liaoning University, Shenyang, 110036, China.

Scientific reports

|July 30, 2024

概括

我们开发了CBIL-VHPLI,这是一种用于预测病毒-宿主蛋白-lncRNA相互作用的深度学习模型. 这种新的方法实现了高精度,有助于了解病毒病原和宿主免疫力.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

病毒-宿主蛋白-lncRNA相互作用 (VHPLI) 对于理解病毒病原和宿主免疫反应至关重要.
之前的VHPLI预测研究集中在植物和动物上,对病毒相互作用的研究有限.

研究的目的:

开发一种用于预测VHPLI的新型深度学习模型,特别关注病毒相互作用.
通过转移学习提高VHPLI预测的准确性和适用性.

主要方法:

一个深度学习模型,CBIL-VHPLI,集成卷积神经网络 (CNN) 和双向长期和短期记忆 (BiLSTM) 网络与转移学习.
使用k-mer,一热编码,CTD和Z曲线方法对蛋白质和lncRNA序列进行特征提取.
在各种数据集上进行模型预训练,然后对病毒-人类 lncRNA 相互作用进行微调.

主要成果:

预训练的CBIL-VHPLI模型在外部验证数据集上实现了大约0.9的准确性.
对病毒蛋白-人类 lncRNA 数据集的微调导致精度提高到 0.946.
该模型以RIP-Seq实验数据显示了91.6%的预测可重现率,并成功预测了PIK3CD-AS2和H5N1 NS1相互作用.

结论:

在预测病毒-宿主蛋白-lncRNA相互作用方面,CBIL-VHPLI代表了重大进展.
该模型的高精度和实验验证强调了其在病毒学中阐明分子机制的潜力.
开发的模型和数据集公开用于学术研究.

关键词:

双向的长期短期记忆.卷积神经网络是一种卷积神经网络.四个序列预处理.LncRNA蛋白相互作用转移学习方法转移学习方法

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