Advancing bioinformatics with large language models: components, applications and perspectives.

ABSTRACT: Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data

SUBMITTER: Liu J 

PROVIDER: S-EPMC10802675 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature