Neural Network · Live Data · Updated Every Retrain
Real embeddings · Real discoveries · Real trust gates
Every time a miner's submission passes validator consensus and lands on-chain, LIFE-BRAIN ingests it. The model learns jointly across all three drug-discovery modalities — small-molecule protein binding, mRNA silencing, and CRISPR guide-RNA efficiency — accumulating a shared embedding space where biochemically similar targets cluster together regardless of which miner or modality contributed the data.
A branch earns trusted status only after passing leave-one-target-out cross-validation against real held-out discoveries — meaning LIFE-BRAIN's predictions for a target it has never seen must outperform the per-modality fallback baseline. Until that bar is cleared, the branch observes, accumulates, and waits. It influences nothing. Every node below represents a real cancer target with at least one validator-confirmed discovery. Every edge represents a real learned similarity between trusted embeddings, projected from 16 dimensions to 3D via SVD. Nothing here is decorative.
The network grows automatically. As new miners join and new targets accumulate confirmed submissions, LIFE-BRAIN retrains, the snapshot updates, and this page reflects the new structure within 30 seconds — no manual intervention required.