AI-powered single-cell biomarker discovery with ScaiVision

The complexity of modern single-cell experiments—often involving millions of data points across various modalities—frequently outpaces traditional analysis methods. ScaiVision was designed to bridge this gap.

By integrating multi-modal single-cell data with specific clinical endpoints, our platform identifies the complex molecular signatures that drive disease progression and therapeutic efficacy. We are looking for partners who possess unique, well-phenotyped datasets and a drive to translate "big data" into actionable medical breakthroughs.

Core Capabilities of ScaiVision:

Our platform moves beyond simple clustering. It utilizes advanced deep learning to extract biological patterns that are often invisible to the naked eye or standard bioinformatics pipelines:

• Multi-Modal Integration: Seamlessly combine single-cell RNA-seq, CITE-seq (surface proteins), and BCR/TCR repertoire data to gain a holistic view of the cellular landscape.

• Endpoint-Driven Discovery: Unlike unsupervised methods, ScaiVision correlates molecular features directly with clinical outcomes—such as drug resistance, disease severity, or long-term survival.

• Unbiased Feature Extraction: Identify rare "ultra-specific" cell states and gene expression programs that serve as robust biomarkers for patient stratification.

• Validated Scalability: Process massive datasets with billions of parameters while maintaining the biological nuance required for precision medicine.