A representation layer built before the map

Most genomic systems begin by mapping sequence to known biology. Alphabiome operates directly on the raw signal, discovering recurring structure before genes, species or annotations are introduced.

From raw signal to biological representation

How alphabiome makes raw genetic signal computable for AI, with Dr. Yaniv Altshuler, nobel Laureate Prof. Roger Kornberg and Prof. Sandy Pentland.

A global vocabulary discovered from data,

not designed by humans

The engine identifies anomalous communities of recurring genetic fragments and consolidates them into a shared vocabulary of more than 100,000 canonical biological tokens. Tokens discovered across different organisms, tissues and biological environments are unified into a reusable global representation.

Built to

learn relationships

not memorize annotations

Our self-supervised architecture learns how biological tokens relate across samples and datasets, creating reusable representations for downstream prediction and discovery.

New questions are modeled on top of the same underlying biological language rather than beginning from raw data each time.

Designed to compound

The vocabulary is periodically refreshed as new data is added, similar to how large AI models are expanded and retrained. More than 100 trillion genetic bases have already contributed to the platform’s growing view of biological structure.