CDPMF-DDA:用于药物-疾病关联预测的对比深度概率矩阵分解
Xianfang Tang1, Yawen Hou1, Yajie Meng1
1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, 430200, China.
BMC bioinformatics
|January 8, 2025
概括
这项研究介绍了CDPMF-DDA,这是一种用于药物疾病关联预测的多视图对比学习框架. 它通过整合多样化的数据表示来改进单一视图方法,以便更准确地发现治疗用途.
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:
- 药物开发是复杂而昂贵的.
- 药物疾病关联 (DDA) 预测识别了现有药物的新用途.
- 现有的单视图对比学习方法在捕捉复杂的药物-疾病关系方面存在局限性.
研究的目的:
- 引入CDPMF-DDA,一个新的多视图对比学习框架,用于增强药物疾病关联预测.
- 提高确定现有药物的新疗法用途的准确性和稳定性.
- 利用多样化的信息表示来更好地理解药物和疾病的相互作用.
主要方法:
- 将药物-疾病关联矩阵分解为药物和疾病特征矩阵.
- 重建了药物-疾病,药物-药物和疾病-疾病相似性网络,以减少噪音.
- 从原始和重建的网络生成多个对比的视图,以捕捉隐藏的特征关联.
主要成果:
- 在三个标准数据集上,CDPMF-DDA的平均AUC为0.9475和AUPR为0.5009,超过了现有的模型.
- 对阿尔茨海默病和的案例研究证实了该模型的有效性和稳定性.
- 多视图方法有效地捕获了复杂的药物疾病关联.
结论:
- 多视图对比学习框架CDPMF-DDA有效地整合了多来源信息,用于DDA预测.
- 该模型表现出高精度和稳定性,使其成为药物重新定位的强大工具.
- 这一框架促进了新治疗策略的发现和药物的重新用途.
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