Advanced exploration platform for multimodal cancer data through vector embeddings. Investigate how aggregated unaligned embedding spaces maintain tumor of origin signals.
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Understanding multimodal cancer data through vector embeddings
This platform is based on groundbreaking research demonstrating that Embeddings from unaligned latent spaces can encode complex biological data into low-dimensional spaces while maintaining relationships between entities.
Signal from unaligned embedded values is conserved and can still be used for learning tasks, such as data modality and tumor of origin recognition.
How high-dimensional data becomes explorable
Practical applications of multimodal vector embedding analysis
Identify tumor of origin by analyzing aggregated embeddings from multiple data modalities (genomics, pathology, clinical data).
Combine unaligned embedding spaces from different data types without losing critical biological signals.
Uncover hidden patterns and relationships in high-dimensional biological data through vector similarity analysis.
Start by creating a collection or exploring existing datasets to discover the power of vector-based biological data analysis.
Advanced features for vector database exploration
High-performance similarity search across high-dimensional embeddings
Interactive 2D projections of high-dimensional vector spaces
Efficient vector database with metadata support
Comprehensive annotation and categorization system