Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms

• URL: http://arxiv.org/abs/2602.08751v2
• Date: Thu, 12 Feb 2026 16:40:32 GMT
• Title: Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms
• Authors: Nobuyuki Ota,
• Abstract summary: We present CDT-II, an "AI microscope" whose attention maps are directly interpretable as regulatory structure.<n>By mirroring the central dogma in its architecture, CDT-II ensures that each attention mechanism corresponds to a specific biological relationship.<n>Applying to K562 CRISPRi data, CDT-II predicts perturbation effects and recovers the GFI1B regulatory network without supervision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current biological AI models lack interpretability -- their internal representations do not correspond to biological relationships that researchers can examine. Here we present CDT-II, an "AI microscope" whose attention maps are directly interpretable as regulatory structure. By mirroring the central dogma in its architecture, CDT-II ensures that each attention mechanism corresponds to a specific biological relationship: DNA self-attention for genomic relationships, RNA self-attention for gene co-regulation, and DNA-to-RNA cross-attention for transcriptional control. Using only genomic embeddings and raw per-cell expression, CDT-II enables experimental biologists to observe regulatory networks in their own data. Applied to K562 CRISPRi data, CDT-II predicts perturbation effects (per-gene mean $r = 0.84$) and recovers the GFI1B regulatory network without supervision (6.6-fold enrichment, $P = 3.5 \times 10^{-17}$). Systematic comparison against ENCODE K562 regulatory annotations reveals that cross-attention autonomously focuses on known regulatory elements -- DNase hypersensitive sites ($201\times$ enrichment), CTCF binding sites ($28\times$), and histone marks -- across all five held-out genes. Two distinct attention mechanisms independently identify an overlapping RNA processing module (80% gene overlap; RNA binding enrichment $P = 1 \times 10^{-16}$). CDT-II establishes mechanism-oriented AI as an alternative to task-oriented approaches, revealing regulatory structure rather than merely optimizing predictions.

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