使用知识图表完成方法的相容性来重新定位药物
Roger Tu1, Meghamala Sinha1, Carolina González1
1Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, United States of America.
bioRxiv : the preprint server for biology
|August 16, 2024
概括
这项研究结合了知识图嵌入和基于路径的方法,以改善药物重定向预测. 该方法增强了药物疾病征兆的证据,解决了现有技术的局限性.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算药理学是一种计算药理学.
- 药物发现 药物发现
背景情况:
- 知识图中的链接预测用于复合疾病关联.
- 现有的方法,包括知识图嵌入 (KGE),缺乏足够的证据来证明药物疾病的征兆.
- 路径走路算法提高了信心,但在证据和处理罕见疾病方面存在局限性.
研究的目的:
- 在一个大型的生物医学知识图表上评估七种用于药物重定位的链接预测方法.
- 将基于路径的推理与KGE方法相结合,以获得更强大的预测.
- 通过增强的证据支持,识别潜在的药物重用指示.
主要方法:
- 评估七个链接预测算法.
- 基于路径的推理和KGE方法的整合.
- 应用到一个全面的生物医学知识图表,用于药物重用.
主要成果:
- 开发了一种结合基于路径和KGE方法的新方法.
- 综合方法为药物疾病的征兆提供了更有力的证据.
- 确定了潜在的药物重用候选人.
结论:
- 结合基于路径和KGE的方法可以提高药物重定向预测的可靠性.
- 该方法解决了个别方法的局限性,包括证据质量和罕见疾病预测.
- 这项研究通过一个特定的药物重用示例来证明实用性.
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