生物医学知识图表学习用于药物重新利用,通过向多个层扩展由关联到多个层的内
Dongmin Bang1,2, Sangsoo Lim3, Sangseon Lee4
1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
Nature communications
|June 15, 2023
概括
这项研究介绍了DREAMwalk,这是一种新的计算药物重新定位方法,使用语义多层过错-通过-关联方法. 它通过在统一的嵌入空间中有效地绘制药物和疾病,提高了药物疾病预测的准确性.
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.
- 药理学 药理学是指药理学的学科.
背景情况:
- 计算药物重新定位使用生物医学知识图表来找到新的药物指示.
- 在这些图表上学习受到基因优势和稀疏的药物/疾病实体的阻碍,导致表现不佳.
研究的目的:
- 通过解决生物医学知识图表学习的局限性,开发一种有效的计算药物再利用方法.
- 为了提高预测药物疾病关联的准确性.
主要方法:
- 提出了一种"语义多层次的指责与关联"的方法.
- 开发了DREAMwalk,一个使用语义信息引导的随机步行来生成节点序列的模型.
- 为药物和疾病生成统一的嵌入式.
主要成果:
- 与最先进的模型相比,药物疾病关联预测准确度提高了16.8%.
- 在嵌入空间中展示了生物和语义上下文之间的和对齐.
- 成功地应用了药物重用案例研究的方法,用于乳腺癌和阿尔茨海默病.
结论:
- 多层次的因关联罪恶感观点为生物医学知识图表上的药物重用提供了一个强有力的策略.
- DREAMwalk有效地绘制药物和疾病的地图,提高预测准确度,并揭示生物洞察力.
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