arXiv：cs.AI· Max Achterweust, Stefan Groot Nibbelink, Vincent J. de Beer, Stefan Schipper, Zeynep Y\"uksektepe, Pedro Andrade da Silva, Aurelia Peppelaar, Martijn J. van der Willigen, Christophe B. Minkenberg, Robert F. van der Willigen·· 16 小时前
PRAISE：让非程序员科学家复现已发表 AI 模型的可复现工作流
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models
AI 导读
研究人员提出工作流 PRAISE（Practical, Reproducible AI-model Implementation for Science and Education），帮助无编程基础的科学工作者逐步实现已发表的 AI 模型，并针对常见失败提供备用路径。三名无编程与数据科学背景的化学专业本科生及其导师据此复现、验证并扩展了一个预测酶-底物相互作用的 SOTA 模型。
正文
Abstract:Artificial Intelligence (AI) models are increasingly developed, published and used across scientific fields. Researchers, students and industry professionals who want to apply these models to their own research questions often lack the programming and data science expertise to do so. Existing guidelines cover single steps, but not the route from a published model to working predictions on one's own data. Here we present the hands-on workflow PRAISE, a Practical, Reproducible AI-model Implementation for Science and Education, that helps non-programmers implement published AI models step by step. Because implementations rarely work the first time, PRAISE anticipates common failures and provides a fallback route for each. As a proof of principle, three BSc chemistry students and their tutor applied PRAISE without prior programming or data science knowledge. They reproduced, validated and extended a state-of-the-art model that predicts enzyme-substrate interactions. This shows that with PRAISE, implementing an AI model from the literature becomes a learnable skill, one that turns non-programmer scientists from users into evaluators and improvers of AI models.
| Comments: | 20 pages, two columns, LaTeX, 5 figures |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.16980 [cs.AI] |
| (or arXiv:2610.16980v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16980 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Stefan Groot Nibbelink [view email]
[v1]
Mon, 12 Oct 2026 15:39:12 UTC (1,126 KB)
来源：arXiv：cs.AI · arxiv.org