TL;DR (Quick Answer) This is an honest engineering write-up of a MOGONET-style multi-omics consensus biomarker pipeline built as an internal R&D project at sysofti . The headline — on a small synthetic cohort (n=30), the graph network alone scores near-random in leak-free 5-fold cross-validation (AUC 0.53 ± 0.16) . Yet as one voter in a 5-evidence consensus , the top-10 ranking is 90% real markers (9 of 10 are known periodontitis genes). The lesson — a single model that looks weak in honest evaluation can still be a useful voter . That contrast is the whole point of the consensus design, and we show it with data. What it is — per-omics Graph Convolutional Networks (GCN) over a sample-similarity graph, attention-fused, contributing to a consensus score alongside differential-expression hubs, Random Forest, a DNN, and co-expression modules. What it is *not * — the official MOGONET. We dropped the original's VCDN fusion for attention fusion.…