# Many bioinformatics programming tasks can be automated with ChatGPT

arXiv: https://arxiv.org/abs/2303.13528 · HTML: https://mlsanity.com/paper/2303.13528/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 12 of 12)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
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- [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023)
- [Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives](https://mlsanity.com/paper/2306.17459/) (2023)
- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics](https://mlsanity.com/paper/2411.18825/) (2024)
- [Humans are Still Better than ChatGPT: Case of the IEEEXtreme Competition](https://mlsanity.com/paper/2305.06934/) (2023)
- [Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses](https://mlsanity.com/paper/2310.15317/) (2023)
- [GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models](https://mlsanity.com/paper/2305.13821/) (2023)
- [An investigation into the scientific landscape of the conversational and generative artificial intelligence, and human-chatbot interaction in education and research](https://mlsanity.com/paper/2407.12004/) (2024)
- [BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining](https://mlsanity.com/paper/2506.21567/) (2025)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)

## Abstract (arXiv)

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model -- OpenAI's ChatGPT -- can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.