可扩展的嵌入融合与蛋白质语言模型:从基准测试文本集成表示的见解
Young Su Ko1, Jonathan Parkinson1, Wei Wang1,2
1Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
整合文本数据和融合嵌入式显著改善了蛋白质语言模型 (pLMs). 一个新的算法有效地结合了嵌入,在关键的生物任务中取得了最先进的结果.
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.
- 机器学习在生物学中的应用
背景情况:
- 蛋白质语言模型 (pLMs) 使用预训练的嵌入式用于生物学的转移学习.
- 标准的pLM目标可能会产生与下游生物任务不一致的表示.
- 增加模型大小并不能保证更好的表示质量.
研究的目的:
- 调查增强蛋白质语言模型表示的策略.
- 提高pLMs在各种生物应用中的实用性.
- 为了解决目前的pLM嵌入策略的局限性.
主要方法:
- 通过对比学习集成生物文本注释来创建文本集成的pLM (tpLM).
- 使用嵌入式融合来组合来自多个plm的表示.
- 开发和应用一个贪的前进选择算法,以有效地嵌入子集识别.
主要成果:
- 在所有测试任务中,没有一个单一的pLM或tpLM在所有测试任务中始终优于其他.
- 在大多数生物任务中,tpLM嵌入的融合提高了性能.
- 贪的前进选择算法有效地识别了接近最佳的嵌入子集,在同源序列恢复和蛋白质-蛋白质相互作用预测方面取得了最先进的结果.
结论:
- 嵌入融合是改善蛋白质表示的实用和可扩展的方法.
- 文本集成和嵌入融合为推进pLM能力提供了互补的策略.
- 开发的贪算法克服了与嵌入融合相关的计算瓶.
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