微调蛋白质语言模型释放了代表性不足的病毒蛋白质组的潜力
Rajan Sawhney1, Barbra D Ferrell2,3, Thibaut Dejean1
1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, Hawai'i, United States of America.
PeerJ
|September 23, 2025
概括
对病毒序列的精细调整蛋白质语言模型 (pLMs) 提高了它们对病毒蛋白质的表示能力,解决了对代表性不足的物种的偏见. 这增强了病毒学和生物技术的下游应用.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 机器学习 机器学习
背景情况:
- 蛋白质语言模型 (pLMs) 为各种应用产生强大的蛋白质嵌入.
- 现有的plm显示对代表性不足的物种,特别是病毒蛋白的偏见,限制了它们的效用.
- 病毒蛋白质至关重要,但在目前的pLM培训数据中代表性不足.
研究的目的:
- 提高pLMs对病毒蛋白质的表示质量.
- 为了减轻pLM中对代表性不足的物种的偏见.
- 提高pLM在涉及病毒蛋白的下游任务上的性能.
主要方法:
- 在各种病毒蛋白序列上微调预训练的pLM.
- 利用各种学习框架和参数有效的微调策略.
- 对学习蛋白质嵌入的质量进行基准测试.
主要成果:
- 微调显著提高了病毒蛋白质表示的质量.
- 增强的表示导致下游计算生物学任务的更好的性能.
- 这种方法有效地解决了对代表性不足的病毒蛋白质组的偏见.
结论:
- 对病毒数据的微调pLM对于准确的病毒蛋白模型至关重要.
- 这项工作为推进病毒生物学研究提供了工具和方法.
- 改进的pLM嵌入式支持疾病控制和生物技术创新的努力.
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