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

翻译：过去十年间，成像技术与分析方法的飞速发展，彻底改变了我们在多尺度上全面探索生物世界的能力，能够精确定位生物分子的类型、数量、位置乃至时间动态。数据复杂性和体量的激增，为将这些海量信息转化为知识带来了重大挑战。近期兴起的多模态大语言模型展现出强大的涌现能力，如理解、分析、推理和泛化。凭借这些能力，MLLM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，从而加速我们的生物学理解，并助力新型计算框架的开发。以往，此类能力主要被认为是人类在全面观察和分析生物图像后，进行解释并总结出有意义结论的专长。然而，当前MLLM的发展显示出其作为智能助手或代理，在生物学研究中增强人类研究者能力的日益增长的前景。