An Evaluation of Large Language Models in Bioinformatics Research
Hengchuang Yin Zhonghui Gu Fanhao Wang Yiparemu Abuduhaibaier Yanqiao Zhu Xinming Tu Xian-Sheng Hua Xiao Luo Yizhou Sun
Large language models (LLMs) such as ChatGPT have gained considerable
interest across diverse research communities. Their notable ability for text
completion and generation has inaugurated a novel paradigm for
language-interfaced problem solving. However, the potential and efficacy of
these models in bioinformatics remain incompletely explored. In this work, we
study the performance LLMs on a wide spectrum of crucial bioinformatics tasks.
These tasks include the identification of potential coding regions, extraction
of named entities for genes and proteins, detection of antimicrobial and
anti-cancer peptides, molecular optimization, and resolution of educational
bioinformatics problems. Our findings indicate that, given appropriate prompts,
LLMs like GPT variants can successfully handle most of these tasks. In
addition, we provide a thorough analysis of their limitations in the context of
complicated bioinformatics tasks. In conclusion, we believe that this work can
provide new perspectives and motivate future research in the field of LLMs
applications, AI for Science and bioinformatics.