An Evaluation of Large Language Models in Bioinformatics Research

TL;DR

This paper evaluates the capabilities of large language models like GPT in performing various bioinformatics tasks, highlighting their successes and limitations to guide future research in AI-driven biological data analysis.

Contribution

It provides a comprehensive assessment of LLMs in bioinformatics, demonstrating their potential and identifying current limitations for complex biological tasks.

Findings

LLMs can successfully perform many bioinformatics tasks with proper prompts.

Limitations exist for complex and specialized bioinformatics problems.

The study offers insights for future AI applications in bioinformatics.

Abstract

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.