Data Dominance
Equalized
Neither GE nor PPI is prioritized at any stage of the decomposition.
CodeFleet bridges the dimensional gap between gene expression and protein interaction through tensor mathematics, structural gating, and infrastructure resilience — so biology can be reasoned about as a network, not a list.
Disease rarely emerges from a single broken molecule. It arises when interactions between thousands of proteins shift in concert. Yet the math we use to find it forces those networks into flat, two-dimensional matrices.
The current bottleneck in computational target discovery is mathematical, not biological. Traditional 2D matrices cannot integrate gene expression (GE) and protein–protein interactions (PPI) on equal footing. Whichever data type you compute against first, the other goes dark.
Filtering by differentially expressed genes first eliminates the topological context of every non-DEG. Structure is lost the moment expression is privileged.
Mapping expression onto pre-defined networks arbitrarily excludes genes lacking documented connections. Discovery is bounded by what is already known.
Higher-Order Singular Value Decomposition bundles multiple biological layers into a single mathematical structure. SVD is applied to network topology and gene expression independently, then projected into a shared tensor space — without assuming priority.
The result is a 3D core tensor in ℝN×10×2 that preserves raw expression alongside the structural strength of every documented interaction. Topology survives across all layers. Equilibrium is enforced by math.
Scale introduces noise. Bundling massive tensors with high-fidelity 3D structural data floods the engine with biological artifacts — predictions lacking structural validation that create false bridges in tensor space.
We use the 1.49M deduplicated sequences of the Synthyra/PINDER dataset as a dynamic structural gate. Each candidate interaction must clear an apo/holo state check before entering the tensor. Sequences under 20 amino acids are removed. Only high-confidence interaction trajectories survive.
The free conformation. Reference geometry against which binding must be measured. Candidates that show no structural shift on contact are rejected as topological artifacts.
The complex conformation. Physical binding confirmed by measurable structural rearrangement. Only holo-validated interactions are admitted to the tensor space.
TCGA RNA-seq and BIOGRID PPI data processed through the full 3D pipeline. The engine isolates the precise topological bottlenecks controlling pathway flow across varying clinical states.