通过相似网络融合和多视图特征投影表示来预测药物疾病关联
IEEE journal of biomedical and health informatics
|August 1, 2023
概括
这项研究引入了一种新的计算模型,通过整合各种数据来预测药物疾病关联 (DDAs). 该模型有效地识别了潜在的新DDA,提高了药物开发效率.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 药物疾病关联 (DDA) 预测对于有效的药物开发至关重要.
- 现有的计算方法往往忽略了未经证实的对中的潜在关联.
- 将多个数据资源集成到异质网络中是一种常见的方法.
研究的目的:
- 提出一种新的计算模型,用于预测新的药物疾病关联 (DDA).
- 解决现有方法在考虑未经证实的与药物或疾病相关的配对方面的局限性.
- 在药物开发中提高DDA预测的准确性和效率.
主要方法:
- 构建一个具有药物,标,细胞系和疾病的异质网络.
- 应用基于更新和合并的相似性网络融合 (UM-SF) 方法.
- 使用中间层介导的多视图特征投影表示 (IM-FP) 方法进行DDA评分.
主要成果:
- 拟议的模型通过比较实验证明了它的有效性.
- 与最先进的模型相比,10倍的交叉验证显示了AUROC和AUPR指标的卓越性能.
- 该模型成功预测了107种新型高等级药物疾病关联.
结论:
- 新的计算模型显著改善了药物疾病关联的预测.
- UM-SF和IM-FP方法为整合多样化的生物数据提供了创新的解决方案.
- 这种方法有望加速药物发现和开发.
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