使用先进的自然语言处理技术破译基因组代码:一个范围审查
Shuyan Cheng1, Yishu Wei1, Yiliang Zhou1
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 25, 2025
概括
自然语言处理 (NLP) 和大型语言模型 (LLM) 正在彻底改变基因组数据分析. 这些先进技术改善了基因组序列的解释和调控元素的预测,为个性化医疗铺平了道路.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 人类基因组测序产生了巨大的,复杂的数据,挑战了传统的分析.
- 自然语言处理 (NLP) 为解释生物序列提供了新的方法.
- 大型语言模型 (LLM) 和变压器架构在这个领域显示出希望.
研究的目的:
- 审查NLP,LLM和转换器在基因组数据分析中的应用.
- 专注于代币化,变压器模型和监管注释预测.
- 在最近的基因组NLP文献中评估数据和模型可访问性.
主要方法:
- 根据PRISMA指南进行的范围审查.
- 在主要的科学数据库 (PubMed,Medline,Scopus,科学网,Embase,ACM数字图书馆) 中进行搜索.
- 包括关于用于基因组测序数据分析的NLP方法的研究,无论出版日期或类型如何.
主要成果:
- 选择了26项研究 (2021年至2024年4月).
- 代币化和变压器模型显著提高了基因组数据处理和理解.
- 应用包括预测监管注释,如转录因子结合点和染色质可访问性.
结论:
- NLP和LLM显示了简化大规模基因组数据解释的巨大潜力.
- 这些技术可以通过高效的基因组分析来推进个性化医疗.
- 需要进一步的研究来解决模型可访问性,可解释性和透明度方面的局限性.
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