Genomic language models with k-mer tokenization strategies for plant genome annotation and regulatory element strength prediction
View abstract on PubMed
Summary
This summary is machine-generated.Optimizing k-mer tokenization strategies significantly boosts genomic language model performance for plant biology tasks. Thoughtful design, not just model size, is key for accurate and efficient in silico analysis.
Area Of Science
- Genomics
- Bioinformatics
- Computational Biology
Background
- Genomic language models (GLMs) offer powerful in silico analysis capabilities.
- Current GLMs often require substantial computational resources.
- K-mer tokenization is a fundamental component of GLMs.
Purpose Of The Study
- To investigate the impact of k-mer tokenization strategies on transformer-based GLM performance in plant genomics.
- To evaluate different k-mer window sizes and overlap schemes.
- To identify optimal tokenization for efficient and accurate genomic sequence modeling.
Main Methods
- Evaluated transformer-based GLMs with varying k-mer window sizes (3-8) and overlap strategies.
- Tested models on plant genomic tasks: splice site prediction and alternative polyadenylation site prediction.
- Compared performance against state-of-the-art models like AgroNT.
Main Results
- K-mer tokenization design critically influences GLM performance, often surpassing the impact of model scale.
- Overlap-based tokenization generally improves performance by retaining local sequence context.
- Non-overlap configurations can achieve competitive accuracy with enhanced computational efficiency for specific tasks.
- A smaller model with optimized tokenization performed comparably to larger, state-of-the-art models.
Conclusions
- K-mer tokenization strategy is a crucial determinant of success in genomic sequence modeling, not solely model size.
- Optimized tokenization enables the development of efficient and high-performing GLMs for plant biology.
- Findings offer practical guidance for designing specialized genomic models.
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