Discover disease-driving targets and responder biomarkers directly from patient data.
Knowledge transfer from large biology
Richer bulk representations
Signal vs. noise disentanglement
Mechanistically grounded outputs
ConvergeCELL™ is trained on a large, diverse multimodal dataset of over 23 million cells from 5,000 patient samples, 550 studies, and 40 clinical indications. This foundation enables robust target and biomarker discovery across disease areas, integrating single-cell and bulk transcriptomic/proteomic data to surface biologically and clinically meaningful insights.
Input
Input Data Integration
Customer expression data (Single-cell or Bulk, Transcriptomics or Proteomics) is processed alongside Converge's proprietary Patient Matrix.
Contrastive Learning
Supervised Contrastive Learning (CL)
A Cell Encoder and Cell Aggregator learn a high-fidelity, unified representation for each patient from their single-cell data, capturing the full biological context rather than disconnected cell signals.
Explainability
Output
