Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein
Biological AI models increasingly predict complex cellular responses, yet their learned representations remain disconnected from the molecular processes they aim to capture. We present CDT-III, which extends mechanism-oriented AI across the full central dogma: DNA, RNA, and protein. Its two-stage Virtual Cell Embedder architecture mirrors the spatial compartmentalization of the cell: VCE-N models transcription in the nucleus and VCE-C models translation in the cytosol. On five held-out genes, CDT-III achieves per-gene RNA r=0.843 and protein r=0.969. Adding protein prediction improves RNA performance (r=0.804 to 0.843), demonstrating that downstream tasks regularize upstream representatio...
| Authors | Nobuyuki Ota |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 26 March 2026 |
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