LEIBER is more than just a name; it is a core philosophy that drives our technology, rooted in the German concept of the "Living Body" (Leib /ˈlaɪbər/ (lye-ber)) and defined by our advanced methodological approach.
Beyond the Physical Body, Toward the Living Life
LEIBER-X ™
Single-cell Intelligence as a Service
Our solution runs on the LEIBER-X™ platform.
It works across all clinical Trial phases & offers adaptive clinical optimization.
Leveraging Extensive Intelligence for precision heuristics Beyond Exploratory Regulatory single-cell strategy
GRN + CNV Explainability
Gold-standard Reproducibility
Audit-ready Traceability
100K+ Archives Reactivated
<2.5 Week Pilots
Validator Sign-off
CDx Champion Enablement
How We De-Risk Your Journey
Transforming bulk tissue data into single-cell precision through AI-powered deconvolution
Single-Cell Core
Data Foundation
WittGen single-cell database with 1.3B+ cells provides the ground truth for all platform capabilities.
Bulk-to-Single Cell GenAI
Affordable Single-Cell Resolution
Transform $50 bulk RNA-seq into $4,000+ single-cell resolution profiles with cellular-level precision.
Image-to-Omics
Image2SC Capability
Derive single-cell omics insights from imaging data to expand analytical capabilities.
Continuous Value Across Clinical Phases
From historical data analysis through
trial design, patient screening & outcome analysis
Superior Data Quality = Superior Results
Our data-centric AI approach prioritizes quality over quantity. Well-curated datasets with precise annotation deliver significantly better model performance.
95% ML Accuracy
vs. 70% market standard
Our Competitive Edge
- CNV inference scoring for cell-level validation
- Manual expert curation + extra information mining
- Golden standard reference dataset construction
- Automated annotation pipeline for scale
UMAP Representation
Real vs Generated Cell Distributions
PDAC (Test Information)
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Real Data
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Generated Data
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Real vs Generated
Virtual Cell Model maintains high performance in complex cancer datasets, scoring MMD ≈ 0.7 / WD ≈ 10.6