Mapping cancer at the cellular level. AI-powered embeddings reveal hidden tumor subtypes, predict drug response, and enable early diagnosis.
Transform single-cell RNA, DNA, and protein data into high-dimensional vectors using our CellGPT neural encoder.
Reduce dimensions with UMAP to reveal natural cell clusters — similar cells group together, just like similar words in NLP.
Match patient tumor embeddings against 100K+ historical cases to predict optimal therapy with unprecedented accuracy.
Captures semantic relationships between words.
Captures biological states, lineages, and disease progression.
Identify rare cancer stem cell populations missed by traditional clustering.
Detect circulating tumor cells (CTCs) with ultra-high sensitivity via liquid biopsy.
Match individual cellular profiles to targeted therapies.
Model the pull of differentiation and metastasis in latent space.
Track disease progression and drug resistance over time.
Translate findings from murine models directly to human biology.
Powered by state-of-the-art machine learning architectures optimized for high-dimensional biological data.