语义增强图形对比学习与适应性否定用于药物重新定位
IEEE journal of biomedical and health informatics
|December 18, 2023
概括
药物重新定位通过处理稀疏的数据集和杂的数据来加速药物开发. 一种新的语义增强图形对比学习方法 (SGCD) 具有自适应性解密,可以提高模型性能和稳定性.
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.
- 药物发现 药物发现
背景情况:
- 传统药物开发是资源密集的.
- 药物重新定位提供了一个有效的替代方案,但面临着挑战.
- 现有的方法在稀疏的数据集和数据噪声中扎.
研究的目的:
- 开发一种先进的药物重新定位模型.
- 解决药物重新定位数据集中的数据稀疏性和噪声问题.
- 为了提高药物重新定位预测的准确性和稳定性.
主要方法:
- 提出了一种语义丰富的增强图形对比学习 (SGCD) 方法.
- 集成了一种适应性无声化技术来处理噪音数据.
- 通过嵌入层和语义邻里挖矿来增强数据.
主要成果:
- SGCD有效地减轻了数据稀疏性的影响.
- 适应性消噪方法提高了模型的抗噪强度.
- 实验证明了拟议模型在真实数据集上的卓越性能.
结论:
- SGCD为药物重新定位提供了一种强大而有效的方法.
- 该方法增强了数据表示和噪声处理能力.
- 这项工作通过改进现有模型来推进计算药物发现.
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