cdsBERT - 扩展蛋白质语言模型与Codon意识
Logan Hallee1, Nikolaos Rafailidis2, Jason P Gleghorn3
1Center for Bioinformatics and Computational Biology, University of Delaware.
bioRxiv : the preprint server for biology
|September 25, 2023
概括
蛋白质语言模型 (pLMs) 现在包含了编码子信息,增强了蛋白质分析. 这种基于编码子的方法,cdsBERT,通过捕捉微妙的序列变化来改善预测.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 蛋白质语言模型 (pLMs) 分析高通量研究的蛋白质序列.
- 的使用偏差越来越多地被认为是因为它对蛋白质结构和功能的预测能力.
结论:
- 基于Codon的pLM提供了一种改善蛋白质结构和功能预测的新方法.
- 未来的生物特异性编码器pLMs可以提高编码器使用忠实度.
- 创建一个编码子pLM基础模型,并通过CDS丰富蛋白质数据库对于推进该领域至关重要.
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