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How it works
Greater accuracy with less ambiguity
Artificial intelligence meets biology
Leveraging artificial intelligence-based technology, DiffDx-Melanoma is an objective test with a high level of accuracy, low technical failure, low intermediate risk, and an inclusion of a variety of both benign nevi and malignant lesions.
35 genes
Neural networks are used to identify 32 genes spanning a range of important biological processes, along with 3 control genes, to best classify melanocytic lesions.
2 algorithms
Two proprietary algorithms are applied to the gene expression patterns to identify the malignant potential of a lesion.
1 result
One clinically actionable result in over 96% of the cases including multiple types of benign nevi and malignant melanoma.
DiffDx-Melanoma genes by function
| Barrier Function | Cytoskeleton | Gene Regulation | Melanin Biosynthesis | Protein Synthesis | Tumorigenesis | Control Genes |
| *CST6 | ABLIM1 | *BAP1 | ATP6V0E2 | RPL37A | ANXA8L1 | *FXR1 |
| CSTA | DSP | GATA3 | DCT | RPS16 | BCL2A1 | *HNRNPL |
| *CLCA2 | KRT2 | KLF5 | GPR143 | *BTG1 | *YKT6 | |
| *GJA1 | KRT17 | *SAP130 | PTN | *CXCL14 | ||
| HAL | NES | SFN | WIPI1 | DUSP4 | ||
| *MGP | *PPL | TP63 | *S100A8 | |||
| *S100A9 |
*Genes also included in DecisionDx-Melanoma assay.
