This study introduces a novel software tool leveraging large language model
(LLM) prompts, designed to automate the generation of academic articles from
Python code a significant advancement in the fields of biomedical informatics
and computer science. Selected for its widespread adoption and analytical
versatility, Python served as a foundational proof of concept; however, the
underlying methodology and framework exhibit adaptability across various GitHub
repo's underlining the tool's broad applicability (Harper 2024). By mitigating
the traditionally time-intensive academic writing process, particularly in
synthesizing complex datasets and coding outputs, this approach signifies a
monumental leap towards streamlining research dissemination. The development
was achieved without reliance on advanced language model agents, ensuring high
fidelity in the automated generation of coherent and comprehensive academic
content. This exploration not only validates the successful application and
efficiency of the software but also projects how future integration of LLM
agents which could amplify its capabilities, propelling towards a future where
scientific findings are disseminated more swiftly and accessibly.

介绍了一种利用大型语言模型的新软件工具，旨在自动从 Python 代码生成学术文章，这对生物医学信息学和计算机科学领域具有重要意义，可大大提高研究传播效率。

科学出版的未来：自动文章生成

The Future of Scientific Publishing: Automated Article Generation

The year 2023 marked a significant surge in the exploration of applying large
language model (LLM) chatbots, notably ChatGPT, across various disciplines. We
surveyed the applications of ChatGPT in various sectors of bioinformatics and
biomedical informatics throughout the year, covering omics, genetics,
biomedical text mining, drug discovery, biomedical image understanding,
bioinformatics programming, and bioinformatics education. Our survey delineates
the current strengths and limitations of this chatbot in bioinformatics and
offers insights into potential avenues for future development.

2023 年，将大型语言模型（LLM）聊天机器人 ChatGPT 应用于各个领域迎来了显著增长；我们调查了 ChatGPT 在生物信息学和生物医学信息学领域的各个部门中的应用情况，涵盖了组学、遗传学、生物医学文本挖掘、药物发现、生物医学图像理解、生物信息学编程和生物信息学教育；我们总结了这个聊天机器人在生物信息学领域的当前优势和局限，并提供了未来发展的潜力方向。

ChatGPT 在生物信息学和生物医学信息学中的应用：第一年回顾

Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review

Introducing biomedical informatics (BMI) students to natural language
processing (NLP) requires balancing technical depth with practical know-how to
address application-focused needs. We developed a set of three activities
introducing introductory BMI students to information retrieval with NLP,
covering document representation strategies and language models from TF-IDF to
BERT. These activities provide students with hands-on experience targeted
towards common use cases, and introduce fundamental components of NLP workflows
for a wide variety of applications.

我们开发了三项活动，向初级生物医学信息学（BMI）的学生介绍了自然语言处理（NLP）的信息检索，从 TF-IDF 到 BERT 的语言模型，这些活动为学生提供了与常见用例相关的实践经验，并引入了 NLP 工作流程的基本组成部分。