Results
Genomarker results: reproducible multi-omics biomarkers for breast, pancreatic, and Alzheimer's disease, plus the companion tools Xarang and HappyReader.
Evidence summary
Genomarker’s programs span liquid biopsy, diagnostics, and target discovery. The current state of the evidence:
| Program | Result |
|---|---|
| Breast cancer | Liquid-biopsy biomarkers, above 95% sensitivity and specificity |
| Pancreatic cancer | Liquid-biopsy biomarkers, above 95% sensitivity and specificity |
| Alzheimer’s disease | Liquid-biopsy biomarkers, above 95% sensitivity and specificity |
| Parkinson’s disease | Biomarkers in the pipeline |
| FSHD (facioscapulohumeral muscular dystrophy) | Biomarkers in the pipeline |
| Glioblastoma | Driver genes and therapeutic targets identified |
| Multiple sclerosis | Driver genes and therapeutic targets identified |
| Lung cancer | Driver genes and therapeutic targets identified |
Two properties tie these results together. First, they come from analyzing all omics types — genomics, transcriptomics, proteomics, and metabolomics — rather than any single layer. Second, they are produced under a reproducibility discipline that most biomarker pipelines lack, which is precisely the problem Genomarker was built to solve.
Publications
A curated list of the publications supporting this work — including Dr. Sayad’s books and papers on real-time machine learning and data mining — is maintained on the About page and on his ResearchGate profile.
Companion tools
Two companion tools extend the platform:
- Xarang — a federated machine-learning toolbox, enabling models to be trained across distributed data without centralizing sensitive data in one place.
- HappyReader — a companion tool of the Genomarker ecosystem.
Together with the platform, they form Bioada’s toolkit for taking multi-omics research from raw data to reproducible, deployable biomarkers.
Request access to Genomarker
Genomarker access is currently provided on request. Tell us about your multi-omics study and we will follow up.