一种社区规范化方法,利用多视图数据来预测药物副作用的频率
Lin Wang1, Chenhao Sun1, Xianyu Xu1
1College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China.
Bioinformatics (Oxford, England)
|August 30, 2023
概括
预测药物的副作用频率对于利益风险评估至关重要. 我们的社区规范化方法 (NRFSE) 使用多视图药物和副作用数据有效预测这些频率.
科学领域:
- 药物监督 药物监督 药物监督
- 计算机化药物安全研究
- 医疗保健中的机器学习
背景情况:
- 准确的药物益处风险评估依赖于确定药物的副作用频率.
- 随机对照试验是这方面的标准,但计算预测频率可以更有效地指导这些试验.
- 预测药物副作用频率仍然是一个重大的计算挑战,现有研究有限.
研究的目的:
- 提出和验证一种用于预测药物副作用频率的新型计算方法.
- 利用药物和副作用的多视图数据来提高预测准确度.
- 开发一种方法,可以通过提供可靠的副作用频率估计,有效指导随机对照试验.
主要方法:
- 开发了一个社区规范化方法 (NRFSE),利用多视图数据.
- 按类权重的非负矩阵分解分解了药物副作用频率矩阵,对未知对使用高斯概率.
- 多视图社区规范化整合了药物和副作用属性,以确保类似实体具有相似的潜在签名,适应性加权属性.
主要成果:
- 与基准数据集上的五种最先进的方法相比,NRFSE显示出更好的预测性能.
- 广泛的实验验证了该方法的有效性.
- 一个独立的营销后副作用测试组进一步证实了NRFSE在现实场景中的有效性.
结论:
- 拟议的NRFSE方法提供了一种可靠的方法来预测药物副作用频率.
- 利用多视图数据和社区规范化可以提高预测的准确性.
- 在帮助药物益处风险评估和指导临床试验设计方面,NRFSE显示出前景.
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