Biostate AI, a molecular diagnostics startup combining next-generation RNA sequencing (RNAseq) with generative AI, announced today it has raised $12 million in a Series A funding round led by Accel. The round also saw participation from Gaingels, Mana Ventures, InfoEdge Ventures, and returning investors Matter Enterprise Partners, Vision Plus Capital, and Catapult Ventures. High-profile angels reminiscent of Anthropic CEO Dario Amodei, 10x Genomics CTO Mike Schnall-Levin, and Twist Bioscience CEO Emily Leproust also backed the corporate.
The brand new funding fuels Biostate’s ambitious goal: to make biology predictable and unlock precision medicine at scale. Very similar to how OpenAI trained ChatGPT on trillions of words to grasp human language, Biostate is training foundation models on billions of RNA expression profiles to learn the “molecular language” of human disease.
A Netflix Model for Molecular Medicine
The startup, founded by MIT and Rice professors-turned-entrepreneurs Ashwin Gopinath and David Zhang, envisions a brand new paradigm for diagnostics. Moderately than offering isolated sequencing services, Biostate uses a Netflix-inspired self-sustaining business model: the corporate processes 1000’s of RNA samples at ultra-low cost, feeds that data right into a proprietary generative AI system, and improves its models with every experiment. The result’s a virtuous cycle—inexpensive sequencing powers higher models, and higher models deliver deeper clinical insight.
said Zhang, CEO of Biostate AI.
The transcriptome—the whole set of RNA molecules in a cell—provides real-time snapshots of human health and disease. Yet historically, full-transcriptome sequencing has been prohibitively expensive and difficult to interpret. Biostate is addressing each problems with a dual approach: radical cost reduction and cutting-edge AI.
Technical Innovations: BIRT, PERD, and Generative AI
On the core of Biostate’s offering are two patented technologies: BIRT (Biostate Integrated RNAseq Technology) and PERD (Probabilistic Expression Reduction Deconvolution). BIRT is a multiplexing protocol that enables simultaneous RNA extraction and sequencing from multiple samples, reducing cost nearly tenfold. PERD, meanwhile, applies novel algorithms to filter out “batch effects”—variability introduced by differences in lab conditions or sample handling—which frequently obscures the biological signal in multi-site studies.
This highly standardized RNAseq pipeline feeds into Biostate’s proprietary foundation model, Biobase, which functions very similar to GPT models in natural language processing. Trained on lots of of 1000’s of transcriptomic profiles across tissue types, disease states, and species, Biobase captures the “grammar of biology”—the underlying patterns of gene expression that outline health and disease.
Just as GPT will be fine-tuned to put in writing essays or summarize legal documents, Biobase will be adapted to detect early cancer reoccurrence, predict drug response in autoimmune disease, or stratify patients in cardiovascular trials. Biostate’s Prognosis AI, built on top of Biobase, already shows promise in forecasting leukemia relapse and is being piloted for multiple sclerosis with the Accelerated Cure Project.
said Gopinath, the corporate’s CTO.
Constructing the World’s Largest RNAseq Dataset
Thus far, Biostate has already sequenced over 10,000 samples for 150+ collaborators, including Cornell and other major institutions. Its goal is to scale that number to lots of of 1000’s of samples annually. This exponential growth is made possible by its low-cost RNAseq process and streamlined data ingestion pipeline, OmicsWeb, which standardizes, labels, and securely stores transcriptomic data across jurisdictions.
The corporate’s cloud infrastructure includes several novel GenAI tools, reminiscent of:
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OmicsWeb Copilot – A natural-language interface for analyzing RNAseq data without code.
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QuantaQuill – An AI assistant that generates publication-ready scientific manuscripts, complete with figures and citations.
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Embedding Surfer – A visualization tool that uncovers hidden biological relationships inside gene expression data.
With offices in Houston, Palo Alto, Bangalore, and Shanghai, Biostate is expanding globally to support a growing network of clinical and academic partners. The startup is already processing each fresh and decades-old tissue samples—helping labs extract insights from previously unusable specimens.
Toward General-Purpose AI for All Diseases
Biostate’s endgame is daring: to create a general-purpose AI able to understanding and guiding treatment across all human diseases. This unifying approach stands in contrast to today’s fragmented biotech landscape, where each condition often requires its own siloed diagnostic tool and therapeutic path.
said Zhang.
By treating biology as a generative system—where today’s molecular state determines tomorrow’s outcomes—Biostate believes it could possibly predict not only current health status, but future disease trajectories and optimal interventions.
What’s Next?
With greater than $20 million raised up to now and a rapidly growing client base, Biostate is accelerating clinical collaborations in oncology, heart problems, and immunology. The corporate’s next milestones include regulatory validation of its predictive models and business scaling of its AI-driven diagnostic tools.
As Gopinath puts it:
If Biostate AI succeeds, the following wave of precision medicine may not only be reactive—it can be predictive, personalized, and powered by generative AI.
