基因组语言模型是所有你需要的吗? 探索对蛋白质下游任务的基因组语言模型
Sam Boshar1, Evan Trop1, Bernardo P de Almeida2
1InstaDeep, Cambridge, MA 02142, United States.
Bioinformatics (Oxford, England)
|August 30, 2024
概括
基因组语言模型 (gLMs) 可以预测蛋白质功能,在某些任务上匹配或超过蛋白质组模型. 一个联合模型和3mer代币化进一步提高了性能,统一了基因组学和蛋白质组学.
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.
- 在基因组学和蛋白质组学领域的机器学习.
背景情况:
- 大型语言模型 (LLM) 在基因组和蛋白质组任务中表现出色.
- 基因组语言模型 (gLMs) 可能可以从DNA序列 (CDS) 预测蛋白质功能.
- 由于对联数据有限,gLM在与蛋白质相关的任务上的表现基本上尚未被探索.
研究的目的:
- 与蛋白质语言模型 (pLMs) 相比,评估gLMs在蛋白质任务上的表现.
- 研究联合基因组-蛋白质组模型和不同的基因组令牌化策略的好处.
- 评估glms在基因组学和蛋白质组学统一方法中的潜力.
主要方法:
- 策划了五个数据集,将蛋白质与它们的编码DNA序列 (CDS) 配对.
- 在这些数据集上评估gLM和pLM,比较检索的CDS与采样策略.
- 训练并解释了联合基因组-蛋白质组模型和一个新的核酸转换器,具有3mer标记化.
主要成果:
- 与pLM相比,gLMs表现出竞争力,在某些情况下,表现优越.
- 使用检索的CDS比采样策略产生了更好的性能.
- 联合模型捕获了互补的序列表示,优于单独的模型.
- 一个3倍令牌化的核酸转换器模型提高了蛋白质任务性能,同时保持了基因组任务性能.
结论:
- gLMs对于蛋白质序列分析是有效的,为pLMs提供了强大的补充.
- 联合基因组-蛋白质组模型通过整合各种序列信息来提供协同效益.
- 优化的代币化 (3mer) 提高了gLM对蛋白质任务的性能,支持统一的生物序列建模方法.
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