Large Language Models for Bioinformatics

TL;DR

This survey reviews the development, applications, and challenges of bioinformatics-specific large language models (BioLMs), emphasizing their transformative potential in disease diagnosis, drug discovery, and vaccine development.

Contribution

It provides a comprehensive analysis of BioLMs' evolution, classification, training, applications, challenges, and future directions in bioinformatics.

Findings

BioLMs are increasingly used in disease diagnosis, drug discovery, and vaccine development.

Key challenges include data privacy, interpretability, biases, and domain adaptation.

Emerging trends point to more sophisticated biological and clinical applications.

Abstract

With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.