16/04/2026
Our Representation Layer Extends to a New Domain: 8 Therapies
Alphabiome extends its reference-free intelligence layer to a new domain, turning raw genetic signal into response prediction across three indications and 8 therapies.
A representation layer built before the map
How Alphabiome makes the biology no reference map can read computable for AI. From CEO Dr. Yaniv Altshuler (MIT), Nobel Laureate Prof. Roger Kornberg (Stanford), and Prof. Sandy Pentland (MIT).
Decoding the Signal Others cannot Compute
How Alphabiome makes the 99% of biology no reference map can read computable for AI. From CEO Dr. Yaniv Altshuler (MIT), Nobel Laureate Prof. Roger Kornberg (Stanford), and Prof. Sandy Pentland (MIT).
Raw Data to Predictive Signatures, Fast
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INGEST. Raw DNA and RNA sequencing data from whole genomes and complex mixed biological samples.
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REPRESENT. Reference-free preprocessing and canonical tokenization.
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LEARN. A self-supervised architecture learns relationships between biological tokens and creates reusable representations.
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APPLY. Build predictive models for new labels, outcomes and biological questions.
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EXPLAIN. Trace discovered signals to interpretable protein sequences and mechanisms.
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.
Most biology has no map
Across the microbial and viral world, almost nothing has a reference genome. That is the overwhelming majority of the signal in any sample, and it stays unreadable
DNA behaves like a language
The "words" of DNA, k-mers, carry statistical structure the way words carry meaning. The engine reads that structure straight from sequence, with no dictionary.
Discovery without labels
No reference, no labels, no assumptions about what matters. The engine learns non-random structure at scale, and the patterns others discard become predictive signatures.
Most biology has no map
Across the microbial and viral world, almost nothing has a reference genome. That is the overwhelming majority of the signal in any sample, and it stays unreadable
DNA behaves like a language
The "words" of DNA, k-mers, carry statistical structure the way words carry meaning. The engine reads that structure straight from sequence, with no dictionary.
Discovery without labels
No reference, no labels, no assumptions about what matters. The engine learns non-random structure at scale, and the patterns others discard become predictive signatures.
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.
Protected and difficult to reproduce.
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Proprietary reference-free algorithms and accumulated token relationships.
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Outcome-linked data and a global biological vocabulary.
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Patented and patent-pending technology across 12 patent filings.
Bring us a biological dataset that existing AI cannot read.
Science & News
06/05/2025
AI That Predicts Drug Response from Raw Genetic Signal
Health Technology Net- Alphabiome’s engine reads raw, unannotated DNA beyond reference genomes, delivering unmatched signal for AI drug-response prediction.
06/05/2025
Alphabiome Unveils Revolutionary AI for Reference-Free Decoding
Alphabiome tokenizes raw genetic sequence that today’s biology AI can’t read, to accurately predict response.
21/11/2025
Peer-Reviewed: Reference-Free AI Validated at Scale
A peer-reviewed study validated Alphabiome’s reference-free AI across 10 farms and 339 animals, predicting response directly from raw DNA signal.
07/05/2025
Alphabiome Raises $8M to Build the Biological AI Layer
Finsmes.com- Alphabiome secures seed funding to expand R&D and build the representation layer between raw genetic signal and biological AI.
04/02/2026
Clinical Study: Our AI Predicts Response Across 7 Biologics
In a leading-hospital study, Alphabiome’s AI predicted response across seven biologic drugs, showing up to 3x higher accuracy than state-of-the-art models.
25/09/2025
Independent Validation: Our Reference-Free AI Predicts Again
A controlled trial validated Alphabiome’s reference-free AI on a second compound- an independent test of the same engine on new raw sequence.
25/11/2025
Predicting Response Straight from Raw DNA Signal
Alphabiome’s reference-free AI now reaches human disease. Our tokenization engine reads raw DNA the rest of biology AI can’t, then predicts response.
10/04/2025
Peer-Reviewed Validation: Cross-Farm Prediction Across 13 Herds
A peer-reviewed study validated Alphabiome’s reference-free AI across 13 commercial dairy herds, predicting additive response directly from raw genetic signal alone.
08/09/2024
From Swarm Math to Biology: Predicting Response from Raw Sequence
Our R&D team carries decentralized swarm mathematics into genomics, predicting feed additive efficacy from rumen sequencing, in CRC Press’s Applied Swarm Intelligence
09/01/2024
The Biological Intelligence Layer for AI
Our white paper sets out the thesis behind Alphabiome, tokenizing raw reference-free DNA into the biological intelligence layer today’s biology AI cannot read.
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