BMFM-RNA: whole-cell expression decoding improves transcriptomic foundation models
📰 ArXiv cs.AI
Whole-cell expression decoding improves transcriptomic foundation models by creating a maximally informative bottleneck
Action Steps
- Pretrain models with whole-cell expression decoding (WCED) instead of masked language modeling (MLM)
- Use a single CLS token embedding to reconstruct the entire gene vocabulary
- Evaluate model performance on downstream metrics to compare WCED and MLM
- Fine-tune models with WCED for specific tasks to achieve better results
Who Needs to Know This
ML researchers and bioinformaticians can benefit from this approach to improve the performance of their models in downstream tasks, particularly in transcriptomic analysis
Key Insight
💡 Whole-cell expression decoding creates a maximally informative bottleneck, leading to better cell representations and downstream task performance
Share This
🧬 WCED outperforms MLM in transcriptomic foundation models!
Key Takeaways
Whole-cell expression decoding improves transcriptomic foundation models by creating a maximally informative bottleneck
Full Article
Title: BMFM-RNA: whole-cell expression decoding improves transcriptomic foundation models
Abstract:
arXiv:2506.14861v2 Announce Type: replace-cross Abstract: Transcriptomic foundation models pretrained with masked language modeling can achieve low pretraining loss yet produce poor cell representations for downstream tasks. We introduce whole-cell expression decoding (WCED), where models reconstruct the entire gene vocabulary from a single CLS token embedding, even with limited inputs, creating a maximally informative bottleneck. WCED consistently outperforms MLM on all downstream metrics despi
Abstract:
arXiv:2506.14861v2 Announce Type: replace-cross Abstract: Transcriptomic foundation models pretrained with masked language modeling can achieve low pretraining loss yet produce poor cell representations for downstream tasks. We introduce whole-cell expression decoding (WCED), where models reconstruct the entire gene vocabulary from a single CLS token embedding, even with limited inputs, creating a maximally informative bottleneck. WCED consistently outperforms MLM on all downstream metrics despi
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