使用词汇进化来预测药物重用
1Information School, University of Sheffield, Sheffield, S1 4DP, UK. judita.preiss@sheffield.ac.uk.
BMC medical informatics and decision making
|April 30, 2024
概括
这项研究引入了词汇演变,以寻找现有药物的新用途. 通过分析科学文献中的词语含义如何随着时间的推移而变化,研究人员可以更有效地识别潜在的药物重用候选人.
科学领域:
- 计算生物学是一种计算生物学.
- 自然语言处理自然语言处理.
- 药理学 药理学是指药理学的学科.
背景情况:
- 传统的药物发现依赖于将来自单独出版物的知识联系起来.
- 这可能导致潜在药物对的过度生成.
- 使用词汇演变的替代方法被探索以提高效率.
研究的目的:
- 调查词汇演变的使用,以检测适合重用药物的药物.
- 利用不断变化的词语语境来识别现有药物的新疗法应用.
主要方法:
- 词嵌入是从按时间顺序排序的MEDLINE出版物中生成的,每隔两个月.
- 每个单词都创建了一个词嵌入的时间序列.
- 临床药物是重点,使用统一医学语言系统 (UMLS) 或SemRep提取的语义三倍数来确定重用状态.
主要成果:
- 深度学习分类模型经过训练并使用5倍交叉验证进行评估.
- 性能达到65%的准确度与UMLS标签和81%的准确度与SemRep标签.
- 这些结果表明了word evolution技术在药物重用检测方面的有效性.
结论:
- 单词进化方法在识别用于重新用途的候选药物方面显示出显著的前景.
- 性能取决于注释方法 (UMLS与SemRep).
- 不同的深度学习架构是最佳的,取决于可用的培训数据数量和注释方法.
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