Interpretable AI for genomic sequence analysis

SWAEV Genomics is building a cloud-based API that lets researchers and biotech companies analyze complex genomic segments with models that physically map out why they make structural decisions. Rather than relying on rigid statistical memorization, our infrastructure strips away the black box to reveal underlying regulatory and structural paradigms natively aligned with physical DNA rules.

SWAEV operates as a specialized expert within larger Mixture of Experts (MoE) networks, functioning as an intrinsic interpretability layer that grounds foundational biological models by exposing the underlying sequence motifs and biophysical properties governing predictions.

Key use cases:

• Multi-scale structural profile stratification (4.1 kbp loops to large macro-TAD blocks)
• In silico variant discovery & virtual deletion knockout validation
• De novo promoter clusters and synthetic boundary element design
• Base-by-base regulatory motif mapping for MoE platforms

We are currently seeking early access partners to validate our platform on proprietary structural datasets.

Our platform extends the open-source SPECTRE‑Wave architecture with structural mapping layers explicitly engineered for genomic sequences. Standard Transformer setups rely on expensive algorithms that scale quadratically with length, severely bounding their window size. By mapping interactions using continuous wave propagation dynamics, we replace raw attention mechanics entirely to achieve an ultra-scalable architecture.

Our infrastructure natively handles massive 1-Megabase sequence windows while tracking physical constraints across distinct, scale-specific receptive fields simultaneously. This grants an immediate performance boost of over 10x during training and 30x during reverse-engineering gradient attribution compared to classic quadratic setups of equal length.

We have successfully validated these mechanics across E. coli, yeast, and human validation windows. In benchmark tests, the model's inner filter weights autonomously mapped directly onto 11 fundamental biophysical properties of DNA, including Helical Twist, Slide, and Bendability, without any manual target hardcoding. It treats DNA as a physical molecule, not just text.

To expand our current local prototyping setup into massive, multi-genome scale pipelines, we are actively setting up compute partnerships leveraging dedicated A100/H100 clusters or Google Cloud TPUs.