用于生物图像分析的多模态大语言模型
Multimodal Large Language Models for Bioimage Analysis
AI总结：
针对生物图像数据复杂度与体量激增带来的信息转化难题，该研究探讨了多模态大语言模型（MLLMs）的理解、推理等涌现能力，指出其有望从多模态生物数据中提取复杂信息，作为智能助手辅助生物学研究。
摘要：
过去十年间，成像技术与分析方法的飞速发展彻底革新了我们在多尺度下全面探究生物世界的能力，能够精准定位生物分子的类型、数量、位置乃至时间动态。数据复杂度与体量的激增，给将海量信息转化为知识带来了巨大挑战。近期兴起的多模态大语言模型（Multimodal Large Language Models, MLLMs）展现出强大的涌现能力，例如理解、分析、推理与泛化能力。凭借这些能力，MLLMs有望从生物图像及通过多种模态获取的数据中提取复杂信息，从而加快我们对生物学的认知，并助力新型计算框架的开发。此前，这类从生物图像的全面观察与分析中解读并总结有意义结论的能力，大多被认为是人类专属的。然而，当前MLLMs的发展显示出其作为智能助手或智能体，在生物学研究中为人类研究人员提供辅助的潜力正日益提升。
英文摘要：
Rapid advancements in imaging techniques and analytical methods over the past decade have revolutionized our ability to comprehensively probe the biological world at multiple scales, pinpointing the type, quantity, location, and even temporal dynamics of biomolecules. The surge in data complexity and volume presents significant challenges in translating this wealth of information into knowledge. The recently emerged Multimodal Large Language Models (MLLMs) exhibit strong emergent capacities, such as understanding, analyzing, reasoning, and generalization. With these capabilities, MLLMs hold promise to extract intricate information from biological images and data obtained through various modalities, thereby expediting our biological understanding and aiding in the development of novel computational frameworks. Previously, such capabilities were mostly attributed to humans for interpreting and summarizing meaningful conclusions from comprehensive observations and analysis of biological images. However, the current development of MLLMs shows increasing promise in serving as intelligent assistants or agents for augmenting human researchers in biology research
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。
