强度:通过使用预训练的深度学习模型嵌入,解决不平衡的药物相互作用风险水平.
Weidun Xie1, Xingjian Chen2, Lei Huang3
1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong; Sir William Dunn School of Pathology, University of Oxford, UK.
Artificial intelligence in medicine
|July 5, 2025
概括
DDIntensity有效地解决了使用深度学习嵌入和LSTM注意力模型的不平衡药物相互作用 (DDI) 数据集. 这种生物信息学方法实现了高精度,改善了DDI风险预测,并发现了新的相互作用.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物基因组学 药物基因组学
背景情况:
- 不平衡的数据集在生物信息学中构成了重大挑战,特别是在预测药物相互作用 (DDI) 风险水平方面.
- 由不平衡数据产生的偏见模型导致代表性不足的阶级表现不佳,阻碍了准确的DDI风险评估.
研究的目的:
- 引入DDIntensity,这是处理不平衡DDI风险级别数据集的新方法.
- 利用预先训练的深度学习嵌入和LSTM注意力模型来提高DDI预测的准确性.
主要方法:
- 使用预训练的深度学习模型 (包括BioGPT) 作为嵌入式生成器.
- 从各种数据类型 (图像,图形,文本) 集成嵌入与LSTM注意力网络.
- 在DDinter和MecDDI数据集上训练并验证了DDI密度模型.
主要成果:
- 生物GPT嵌入产生的性能优越,达到0.97的曲线下的面积 (AUC) 和0.92.9的精度回忆曲线下的面积 (AUPR).
- 在不同的DDI数据模式中展示了高可扩展性.
- 通过对瘤学药物 (索拉费尼布,米托克桑) 的案例研究,成功发现了新的药物相互作用.
结论:
- 对于不平衡的生物信息学数据集,DDIntensity提供了一个强大的解决方案,特别是在DDI风险预测中.
- 这种方法提高了模型性能,并促进了新DDI的发现.
- 预先训练有素的深度学习嵌入对于改善DDI风险级别分类至关重要.
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