用大型语言模型推进生物信息学：组件、应用与展望
Advancing bioinformatics with large language models: components, applications and perspectives
AI总结：
本文探讨了大型语言模型在生物信息学中的应用，涵盖其核心组件、关键技术和实际应用，并提出优化策略以推动该领域的发展。
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，其在各种任务中表现出色，尤其是在自然语言处理（NLP）方面。LLMs通常由包含大量参数的人工神经网络组成，通过使用自监督或半监督学习在大量未标记输入上进行训练。然而，它们在解决生物信息学问题方面的潜力可能甚至超过其在建模人类语言方面的专业能力。在本文综述中，我们将全面概述生物信息学中LLMs的核心组件，涵盖基因组学、转录组学、蛋白质组学、药物发现和单细胞分析等领域。涵盖的关键方面包括适用于不同数据类型的分词方法、transformer模型的架构、核心注意力机制以及这些模型的基础预训练过程。此外，我们将介绍目前可用的基准模型，并突出它们在各种生物信息学领域中的下游应用。最后，基于我们的经验，我们将为LLM用户和开发者提供实用的指导，强调优化其使用策略，以促进该领域进一步的创新。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
