VaxjoGNN:一个图形神经网络的本体学基础的疫苗辅助剂建议
1Chinese University of Hong Kong, Shenzhen, Guangdong, China.
bioRxiv : the preprint server for biology
|December 12, 2025
概括
我们开发了VaxjoGNN,这是一种人工智能工具,用于对疾病的疫苗辅助剂进行排名. 它改善了对新疾病的辅助剂选择,加速了疫苗开发.
科学领域:
- 生物医学信息学 生物医学信息学
- 疫苗学 疫苗学 疫苗学
- 人工智能的人工智能
背景情况:
- 辅助剂的选择是疫苗开发中的一个主要挑战,特别是对于数据有限的新兴疾病.
- 现有的计算工具主要关注抗原发现,而不是辅助剂排名.
- 优先考虑有效的辅助剂对于快速开发疫苗至关重要.
研究的目的:
- 开发一个计算框架,根据疾病背景对疫苗辅助剂进行排名.
- 为解决在疫苗设计中对人工智能驱动的辅助剂选择的未满足需求.
- 提高疫苗研发的效率和速度.
主要方法:
- 使用异质知识图,将疾病辅助剂选择作为top-k推任务.
- 提出了VaxjoGNN,一个图形神经网络,集成生物医学本体学,途径和文本.
- 实施了一个新的listwise培训目标,使用ApproxNDCG和ListNet替代损失来优化排名.
主要成果:
- 在已见疾病方面,VaxjoGNN获得了0.59的NDCG@10.
- 该模型对新型疾病具有强烈的概括性,其表现比随机基线高5.4倍 (NDCG@10=0.27).
- 按列表进行培训的目标是提高排名稳定性和表现.
结论:
- VaxjoGNN提供了一个有效的AI驱动的框架,用于优先考虑疫苗辅助剂.
- 基于本体学的方法加速了对已知和新出现的疾病的辅助剂选择.
- 这种方法有可能显著加快疫苗研发时间表.
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