生物信息学中的大型语言模型:应用与前景。

ArXiv Pub Date : 2025-01-31
Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou
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引用次数: 0

摘要

大型语言模型(LLMs)是一类基于深度学习的人工智能模型,在各种任务中,尤其是在自然语言处理(NLP)中表现出色。大型语言模型通常由具有大量参数的人工神经网络组成,通过自监督或半监督学习在大量无标记输入上进行训练。然而,这些模型在解决生物信息学问题方面的潜力甚至可能超过它们在人类语言建模方面的能力。在这篇综述中,我们将总结自然语言处理中使用的著名大型语言模型,如 BERT 和 GPT,并重点探讨大型语言模型在生物信息学中不同 omics 层面的应用,主要包括大型语言模型在基因组学、转录组学、蛋白质组学、药物发现和单细胞分析中的应用。最后,本综述总结了大型语言模型在解决生物信息学问题方面的潜力和前景。
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Advancing bioinformatics with large language models: components, applications and perspectives.

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field.

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