基础模型在生物信息学中的进展与机遇
Progress and Opportunities of Foundation Models in Bioinformatics
AI总结：
本文系统综述了基础模型在生物信息学中的进展，涵盖序列分析、结构预测、功能注释和多模态整合，并讨论了数据噪声、可解释性等挑战及未来发展方向。
摘要：
生物信息学见证了随着人工智能（AI）的日益整合，特别是通过采用基础模型（FMs）而发生的范式转变。这些AI技术迅速发展，解决了生物信息学中的历史性挑战，如标注数据稀缺和数据噪声的存在。FMs特别擅长处理大规模、未标注的数据，这在生物学背景下是常见情况，因为通过实验确定标注数据既耗时又昂贵。这一特性使得FMs在各种下游验证任务中表现出色并取得显著成果，展示了它们有效表示多样化生物实体的能力。毫无疑问，FMs开启了计算生物学的新时代，尤其是在深度学习领域。本综述的主要目标是对生物信息学中的FMs进行系统性的调查和总结，追溯其演变、当前研究状态以及所采用的方法。我们关注的核心是将FMs应用于特定的生物学问题，旨在指导研究社区根据其研究需求选择合适的FMs。我们深入探讨了当前问题的具体方面，包括序列分析、结构预测、功能注释和多模态整合，并与传统方法比较了结构和进展。此外，该综述分析了FMs在生物学中面临的挑战和局限性，如数据噪声、模型可解释性和潜在偏见。最后，我们概述了FMs在未来生物学研究中的潜在发展路径和策略，为这一快速发展的领域中的持续创新和应用奠定基础。这篇全面的综述不仅作为学术资源，也作为未来在生物学中探索和应用FMs的路线图。
英文摘要：
Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly adept at handling large-scale, unlabeled data, a common scenario in biological contexts due to the time-consuming and costly nature of experimentally determining labeled data. This characteristic has allowed FMs to excel and achieve notable results in various downstream validation tasks, demonstrating their ability to represent diverse biological entities effectively. Undoubtedly, FMs have ushered in a new era in computational biology, especially in the realm of deep learning. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. We delve into the specifics of the problem at hand including sequence analysis, structure prediction, function annotation, and multimodal integration, comparing the structures and advancements against traditional methods. Furthermore, the review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases. Finally, we outline potential development paths and strategies for FMs in future biological research, setting the stage for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also as a roadmap for future explorations and applications of FMs in biology.
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。
