基于双变异自编码器预测新的药物指示.
Zhaoyang Huang1, Shengjian Chen1, Liang Yu1
1School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Computers in biology and medicine
|July 24, 2023
概括
我们开发了DIDVAE,这是一种新的深度学习算法,用于预测药物与疾病的关联. 这种方法通过更有效地识别现有药物的新治疗指示来改善药物重新用途.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 药物开发是昂贵和漫长的,很少有新的治疗方法能够达到患者.
- 识别药物疾病相关性对于药物发现和重新使用至关重要.
- 大型生物数据库和深度学习,特别是像变化自编码器 (VAE) 这样的深度生成模型,在这个领域越来越多地被使用.
研究的目的:
- 提出一种新的深度学习算法,DIDVAE (基于双变异自编码器预测新的药物指示),用于预测药物和疾病的关联.
- 利用VAE进行无监督学习,以确定现有药物的潜在新用途.
主要方法:
- 开发了DIDVAE算法,一个双变量自编码器模型.
- 在已知的药物疾病数据上训练模型,以学习隐性变量分布.
- 在统一的数据集上,与已建立的算法 (BBNR,DrugNet,MBiRW,DRRS) 进行了对比DIDVAE的性能.
主要成果:
- 与现有方法相比,DIDVAE算法展示了优越的预测性能.
- 实验结果显示,药物疾病关联的预测准确性总体上有所改善.
- 进一步的分析证实了DIDVAE在预测未知的药物疾病联系方面的实用性.
结论:
- 迪德瓦 (DIDVAE) 提供了一种有前途的计算方法,用于预测药物指示.
- 该算法有效地识别了新的药物疾病关联,支持药物重新定位的努力.
- 这种方法有可能加速发现新的疾病治疗方法.
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