Gene-LLMs: a comprehensive survey of transformer-based genomic language models for regulatory and clinical genomics
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Summary
This summary is machine-generated.Genome large language models (Gene-LLMs), a new NLP application, interpret genomic data for bioinformatics. These models analyze nucleotide sequences and gene expression, revolutionizing functional genomics and clinical diagnostics.
Area Of Science
- Bioinformatics and Computational Biology
- Genomics and Molecular Biology
- Artificial Intelligence in Life Sciences
Background
- Natural Language Processing (NLP) and genomics are converging.
- Transformer-based models, termed Genome Large Language Models (Gene-LLMs), are emerging.
- Gene-LLMs interpret genomic data using nucleotide sequences, gene expression, and multi-omic annotations via self-supervised pretraining.
Purpose Of The Study
- To provide a comprehensive overview of the Gene-LLM lifecycle.
- To detail the applications and impact of Gene-LLMs in various biological fields.
- To outline future directions for Gene-LLM development and application.
Main Methods
- Review of Gene-LLM lifecycle stages: data ingestion, tokenization (k-mer, gene-level), and pretraining tasks (masked nucleotide prediction, sequence alignment).
- Analysis of Gene-LLM applications: enhancer/promoter identification, chromatin state modeling, RNA-protein interaction prediction, synthetic sequence generation.
- Evaluation of Gene-LLM impact using benchmarks (CAGI5, GenBench, NT-Bench, BEACON) in functional genomics, clinical diagnostics, and evolutionary inference.
Main Results
- Gene-LLMs demonstrate significant impact across functional genomics, clinical diagnostics, and evolutionary inference.
- Recent advances include encoder-decoder modifications and positional embeddings for enhanced interpretability and translational potential.
- Benchmarks show the effectiveness of Gene-LLMs in diverse genomic tasks.
Conclusions
- Gene-LLMs are a cornerstone technology for the future of biomedicine.
- Future pathways include federated genomic learning, multimodal sequence modeling, and low-resource adaptation for rare variant discovery.
- Gene-LLMs offer a proactive approach to responsible biomedical innovation.
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