SCALEX Clinical Gate
Authorized clinical operators only. Access is strictly audited under HIPAA guidelines.
ReviveAI: Large Scalable Vector Embeddings (SCALEX) for Drug Repurposing
| Drug Name | Protein Target | Gene Identifier | Primary Disease Group |
|---|---|---|---|
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This dataset is constructed by programmatically aggregating, normalising, and mapping entries across the following biological resources using Python standard library scripts:
Consistent naming conventions are standardized to HGNC for genes, MeSH/UMLS for diseases, and UniProt for protein sequences to prevent graph entity duplication.
Interactive 2-hop topological network visualization. Click nodes to focus the network around its local molecular signaling environment.
Graph embeddings are derived using a 2-layer HeteroGCN architecture. In contrast to flat vectors, structural relationships are preserved via translating translation vectors (BiTrans Aggregation) representing complex pharmacological relationships.
Select a target drug and disease. The ReviveAI dual-stage network will query semantic biomedical literature representations and combine them with topological graph embeddings.
Persist clinical evaluations, trial outcomes, or compliance warnings directly into local Mem0 semantic database.
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Query Prompt: --
Real-time loss developments and model metrics tracked across k-fold structural partitions. Known therapeutic links under test are removed dynamically to prevent data leaks.