通过使用卷积神经网络整合药物疾病关联数据,创建一个新的高效药物重定向框架
Ramin Amiri1, Jafar Razmara2, Sepideh Parvizpour3,4
1Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
BMC bioinformatics
|November 22, 2023
概括
这项研究介绍了IDDI-DNN,这是一个深度神经网络模型,用于高效的药物重定位. 它准确地预测了新的药物疾病关联,克服了传统药物发现方法的局限性.
科学领域:
- 计算生物学 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 药物发现
背景情况:
- 药物重新定位为新的药物发现提供了一个更快,更便宜的替代方案.
- 传统方法是昂贵的,耗时的,并且失败率很高.
- 数据驱动的方法正在出现,用于识别针对特定疾病的候选药物.
研究的目的:
- 提出一个深度神经网络模型,IDDI-DNN,用于有效的药物重定位.
- 整合多样化的药物,疾病和关联数据,以提高预测.
- 使用先进的计算方法识别新的药物疾病关联.
主要方法:
- 构建了药物特性,疾病特性和药物-疾病关联的相似性矩阵.
- 使用两步相似网络融合方法集成这些矩阵.
- 采用卷积神经网络来预测未知的药物疾病关联.
主要成果:
- IDDI-DNN模型显示出高预测准确度.
- 对两个数据集 (黄金标准和DNdataset) 进行了比较评估.
- 在预测药物与疾病的关联方面,IDDI-DNN超越了现有的最先进的方法.
结论:
- IDDI-DNN是药物重用的一个强大的工具.
- 该模型有效地利用综合数据进行准确的药物疾病关联预测.
- 这种方法加快了对现有药物的潜在新疗法用途的识别.
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