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RT-Transformer:对代谢物注释的保留时间预测,以协助代谢物识别
Jun Xue1,2, Bingyi Wang3,4, Hongchao Ji2
1School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650500, China.
Bioinformatics (Oxford, England)
|February 25, 2024
概括
RT-Transformer是一个新的深度学习模型,可以准确地预测不同的方法的液体色谱保留时间. 这一进步通过提高预测可扩展性,提高了非目标代谢学中的代谢物识别.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 分析化学是一种分析化学.
背景情况:
- 精确预测液态色谱 (LC) 保留时间对于在非目标代谢学中代谢物识别至关重要.
- 现有的保留时间预测方法在各种染色学条件下难以扩展.
- 这种局限性阻碍了不同实验设置之间的预测模型的可靠转移.
研究的目的:
- 开发一种新的深度学习模型,RT-Transformer,能够预测在各种染色学条件下LC保留时间.
- 解决模型可转移性的挑战,并提高保留时间预测的可扩展性.
- 在代谢学研究中提高代谢物识别的准确性和效率.
主要方法:
- 开发了RT-Transformer,这是一个集成图形注意网络和1D-Transformer架构的深度神经网络.
- 预先训练的RT-变压器在一个大数据集上有80,038个小分子保留时间.
- 利用转移学习来微调预训练模型在特定的染色学数据集上.
主要成果:
- 与各种数据集的最先进方法相比,RT-Transformer在各种数据集上实现了竞争性性能.
- 该模型展示了出色的可扩展性,成功地预测了5种不同的染色学条件和41个外部数据集的保留时间.
- 预测保留时间的平均绝对误差在不取样的情况下为33.41.
结论:
- RT-Transformer提供了一个可扩展的解决方案,用于预测液体色谱保留时间.
- 该模型通过克服以前预测方法的局限性,显著提高了代谢物识别的准确性.
- RT-Transformer为推进非目标代谢学研究提供了一个有价值的工具.
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