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大型语言模型及其在生物信息学中的应用

Oluwafemi A Sarumi^1,2, Dominik Heider^1,2

¹University of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus, Münster, 48149, Germany.

Computational and structural biotechnology journal

|October 22, 2024

概括

大型语言模型 (LLM) 正在推进自然语言处理 (NLP) 并改变生物信息学. 这些强大的AI工具通过分析复杂的生物数据来加速药物发现和生命科学创新.

科学领域:

生物信息学和计算生物学
人工智能的人工智能
基因组学和蛋白质组学

背景情况:

由于大型语言模型 (LLM),自然语言处理 (NLP) 取得了显著进展.
基于具有数十亿参数的变压器架构的LLM在理解上下文和顺序信息方面表现出色.
这些模型在各种语言任务中提供了高精度.

研究的目的:

审查LLMs研究中的当前趋势.
探索生物信息学LLM的革命性潜力.
突出LLM如何加速生命科学领域的新发现.

主要方法:

对大型语言模型 (LLM) 及其应用的近期进展进行审查.
分析变压器架构及其在NLP中的作用.
检查LLMs对生物信息学挑战的影响.

主要成果:

在生物信息学中,LLM显示出了显著的前景,解决了大型生物数据集的挑战.
应用范围涵盖基因组学,蛋白质组学和个性化医学,包括模式识别和蛋白质结构预测.
通过准确的分子相互作用预测,LLM对于推动药物发现至关重要.

结论:

士学位准备彻底改变生物信息学,加速生命科学发现.
法学士的能力为基因组学,蛋白质组学和个性化医学研究提供了新的途径.
对LLM的持续研究将推动生物数据分析和药物开发方面的创新.

关键词:

生物信息学是一种生物信息学.大型语言模型.自然语言处理自然语言处理.奥米克斯数据数据的数据.

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