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DPSP:一个多式联网深度学习框架,用于多药副作用预测.
Raziyeh Masumshah1, Changiz Eslahchi1,2
1Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran.
确定药物相互作用 (DDI) 的不良影响对于患者的安全至关重要. DPSP框架有效地利用新药特征和深度神经网络预测多药副作用,优于现有方法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 意想不到的药物相互作用 (DDI) 对健康构成重大风险.
- 识别多药药的不良影响是人类健康的一个关键挑战.
- 预测多种药物的副作用的计算方法已经进步.
研究的目的:
- 介绍DPSP,这是预测多药副作用的新框架.
- 为DDI预测开发一种深度神经网络方法.
- 为了产生新的药物特征,以改善DDI识别.
主要方法:
- 使用Jaccard相似性的药物信息评估和特征提取.
- 通过结合相似之处来生成新药特征载体.
- 应用多模式深度神经网络框架用于DDI预测.
主要成果:
- 在基准数据集上,DPSP在GNN-DDI,MSTE和DNN等既定方法相比表现优越.
- 该框架在各种分类指标上实现了高精度.
- 多种药物信息的整合被证明是有效的DDI不良影响的识别.
结论:
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- DPSP框架为预测多药副作用提供了有效和高效的解决方案.
- 利用多样化的药物信息可以提高DDI预测的准确性.
- 该研究强调了深度学习在缓解DDI风险方面的潜力.