Leveraging Fine-Tuned Large Language Models in Bioinformatics: A Research Perspective

Abstract

Bioinformatics synergizes biology, computer science, and statistics and is further propelled by the integration of deep learning and natural language processing (NLP). This analysis extensively explores the applications of fine-tuned language models within bioinformatics, providing empirical evidence and unique perspectives on the impact, challenges, and limitations in this field. The broad scope includes biomedical literature analysis, drug discovery, clinical decision support, protein structure prediction, and pharmacovigilance, among others. This analysis underscores the need to overcome hurdles such as data availability, domain-specific knowledge, bias, interpretability, resource efficiency, ethical implications, and validation for a reliable application of these models. Collaborative efforts between computational and experimental biologists, ethicists, and regulatory bodies are vital to establish ethical guidelines and best practices for their use.