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

Abstract

Bioinformatics, an interdisciplinary field combining biology, computer science, and statistics, has advanced with deep learning and natural language processing techniques. This perspective explores the applications of fine-tuned language models in bioinformatics, highlighting their potential in various domains while discussing challenges and limitations. Fine-tuned language models benefit biomedical literature analysis, extracting information from scientific papers to synthesize knowledge and generate synthetic sequences for DNA, RNA, and protein research. In drug discovery, these models can identify novel drug targets, accelerate virtual screening, and aid drug repurposing by finding new therapeutic indications for existing drugs. For clinical decision support, fine-tuned language models can analyse patient data, medical literature, and guidelines to provide personalized recommendations and alerts to healthcare professionals. They can also aid accurate protein structure prediction for drug design and target identification. In pharmacovigilance, these models can analyse unstructured data sources to detect adverse events from social media, patient forums, and health records, enabling early intervention and improving patient safety. However, challenges like data availability, domain-specific knowledge, bias, interpretability, resource efficiency, ethics, and validation must be addressed for reliable application. Addressing these challenges will unlock the full potential of fine-tuned language models in bioinformatics, driving advancements and benefiting human health. Collaboration between computational and experimental biologists, ethicists, and regulatory bodies is crucial to establish ethical guidelines and best practices for their use.