Wang Jinge, Ye Qing, Liu Li, Guo Nancy Lan, Hu Gangqing
Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV, 26506, USA.
West Virginia University Cancer Institute, West Virginia University, Morgantown, WV, 26506, USA.
NPJ Precis Oncol. 2024 Apr 5;8(1):84. doi: 10.1038/s41698-024-00576-z.
Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting basic bioinformatics data analyses. The recent feature of accepting image inputs by ChatGPT, also known as GPT-4V(ision), motivated us to explore its efficacy in deciphering bioinformatics scientific figures. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when color perception and quantitative analysis of visual elements were involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.
新兴研究强调了基于大语言模型的聊天机器人在进行基础生物信息学数据分析方面的潜力。ChatGPT最近具备的接受图像输入的功能,即GPT-4V(ision),促使我们探索其在解读生物信息学科学图表方面的功效。我们通过癌症研究中的实例进行评估,包括测序数据分析、基于多模态网络的药物重新定位以及肿瘤克隆进化,结果显示ChatGPT能够熟练解释不同类型的图表,并运用生物学知识丰富解读内容。然而,当涉及视觉元素的颜色感知和定量分析时,它在提供准确解读方面存在困难。此外,虽然聊天机器人可以起草图表说明并总结图表中的发现,但必须进行严格校对以确保内容的准确性和可靠性。