为药物重新定位而进行自适应性失调学习
Yajie Meng1, Yi Wang1, Xinrong Hu1
1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, 430200, Hubei, China.
Journal of biomedical informatics
|May 19, 2025
概括
本研究介绍了DRDM,这是一种深度学习框架,用于解决药物重新定位数据集中的偏差. 通过专注于代表性不足的实体,DRDM增强了新药用途的发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 药物重新定位通过确定现有药物的新用途来加速制药发展.
- 图形神经网络 (GNN) 深度学习方法对药物重新定位有希望.
- 现有的药物重新定位数据集显示节点两极分化,具有流行的和长尾实体,可能引入偏差.
研究的目的:
- 分析药物重新定位数据集的固有偏差.
- 提出一种新的深度学习框架,DRDM,以减轻药物重新定位中的数据偏差.
- 增强长尾实体对新药发现洞察力的代表性.
主要方法:
- 对三种常见药物重新定位数据集的分析,以确定节点极化.
- 开发DRDM,一个深度学习框架,带有退化机制.
- 实施动态权重调整以解决对受欢迎实体的偏见.
- 整合双视图对比学习以提高模型稳定性.
主要成果:
- 在分析的药物重新定位数据集中确定了一致的节点偏向.
- DRDM有效地减轻了与受欢迎实体相关的偏见,改善了长尾实体的代表性.
- 实验结果显示DRDM在现有模型中具有强大的竞争力.
- 案例研究表明DRDM在药物发现方面的实际潜力.
结论:
- 节点极化是药物重新定位数据集的关键特征,需要缓解偏差.
- DRDM提供了一种有效的深度学习方法,以解决数据偏差并增强药物重新定位.
- 拟议的框架有可能揭示新的药物向关联,并加速药物发现.
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