Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

2025-11-05

AI Summary

This study systematically evaluates the evolution, application efficacy, and methodological advances of generative AI (GenAI) in bioinformatics. Addressing six core questions—including “How does GenAI enhance accuracy and mechanistic interpretability in multi-omics analysis?”—it conducts a PRISMA-compliant literature review (2018–2024), integrating diffusion models, Transformer architectures, and heterogeneous biological data sources (e.g., UniProtKB, CELLxGENE). Results show that domain-specific pretrained models (e.g., ESM, AlphaFold3-derived architectures) significantly outperform general-purpose models in structural modeling, functional prediction, and synthetic data generation—particularly in context-aware representation learning and multimodal integration—thereby improving molecular representation fidelity. However, critical bottlenecks persist: limited scalability and pervasive data bias. The study proposes a novel paradigm—“biological-mechanism-driven generative modeling”—establishing a methodological foundation and technical roadmap for interpretable, verifiable GenAI–bioinformatics integration.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative artificial intelligence (GenAI) has become a transformative approach in bioinformatics that often enables advancements in genomics, proteomics, transcriptomics, structural biology, and drug discovery. To systematically identify and evaluate these growing developments, this review proposed six research questions (RQs), according to the preferred reporting items for systematic reviews and meta-analysis methods. The objective is to evaluate impactful GenAI strategies in methodological advancement, predictive performance, and specialization, and to identify promising approaches for advanced modeling, data-intensive discovery, and integrative biological analysis. RQ1 highlights diverse applications across multiple bioinformatics subfields (sequence analysis, molecular design, and integrative data modeling), which demonstrate superior performance over traditional methods through pattern recognition and output generation. RQ2 reveals that adapted specialized model architectures outperformed general-purpose models, an advantage attributed to targeted pretraining and context-aware strategies. RQ3 identifies significant benefits in the bioinformatics domains, focusing on molecular analysis and data integration, which improves accuracy and reduces errors in complex analysis. RQ4 indicates improvements in structural modeling, functional prediction, and synthetic data generation, validated by established benchmarks. RQ5 suggests the main constraints, such as the lack of scalability and biases in data that impact generalizability, and proposes future directions focused on robust evaluation and biologically grounded modeling. RQ6 examines that molecular datasets (such as UniProtKB and ProteinNet12), cellular datasets (such as CELLxGENE and GTEx) and textual resources (such as PubMedQA and OMIM) broadly support the training and generalization of GenAI models.

Problem

• Evaluating GenAI strategies for methodological advancement and predictive performance in bioinformatics
• Identifying specialized model architectures that outperform general-purpose models through targeted training
• Addressing constraints like scalability issues and data biases affecting model generalizability

Innovation

• Adapted specialized model architectures for bioinformatics
• Targeted pretraining and context-aware strategies
• Integrative biological analysis with improved accuracy