Large Language Models for Bioinformatics

2025-01-10

AI Summary

Bioinformatics large language models (BioLMs) face fundamental challenges—including unclear distinctions from general-purpose LLMs, lack of standardized evaluation frameworks, and poor clinical applicability. Method: We propose a comprehensive analytical framework featuring a four-dimensional biomedical evaluation schema (privacy/security, interpretability, data/result bias, and cross-domain generalization). Leveraging Transformer architectures, we integrate domain-adaptive pretraining, multimodal sequence modeling, knowledge-enhanced fine-tuning, and clinical semantic alignment across heterogeneous data sources (genomic, proteomic, literature, and electronic health records). Contribution/Results: We systematically survey over 120 BioLMs, establish a unified taxonomy and benchmark suite, identify five persistent bottlenecks, and articulate an evolutionary pathway toward trustworthy, interpretable, and clinically ready BioAI—providing foundational methodology for next-generation biomedical AI.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Large Language Models
• Bioinformatics
• Challenges and Applications

Innovation

• Biological Informatics
• Large Language Models
• Disease Diagnosis and Drug Discovery