BioState AI raises $12 million in Series A to train CHATGPT for Molecular Medicine

BioState AI, a molecular diagnostics startup that combines next-generation RNA sequencing (RNASEQ) with generated AI, announced today that it has raised $12 million in Accel-led Series A financing. The round also saw participation from Gaingels, Mana Ventures, Infoedge Ventures, as well as return investors Matter Venture Partners, Vision Plus Capital and Catapult Ventures. High-profile angels such as Humane CEO Dario Amodei, 10x Genomics CTO Mike Schnall-Levin, and Twist Bioscience CEO Emily Leproust also supported the company.

New funding provides BioState’s ambitious goal: to make biology predictable and unlock precise drugs on a large scale. Just like how changpt with open-trained understanding of human language on trillions of words, Biostate is the basic model of training to understand billions of RNA expression profiles to learn the “molecular language” of human diseases.

Netflix Molecular Medicine Model

Founded by MIT and Rice professors transformed into entrepreneurs Ashwin Gopinath and David Zhang, the startup envisions a new diagnostic paradigm. Instead of providing isolated sequencing services, BioState uses a Netflix-inspired self-sustaining business model: the company processes thousands of RNA samples at ultra-low cost, feeds data into proprietary generative AI systems, and improves its model with each experiment. The result is a good cycle – better sequencing capabilities are achievable, and better models provide deeper clinical insights.

“Every diagnosis I have established is about moving the answer to the close-range aspect of the patient,” explain openCEO of Biostate AI. “BioState made the biggest leap by making the entire transcriptome affordable.”

The transcriptome (a complete set of RNA molecules in cells) provides real-time snapshots of human health and disease. Historically, however, whole-tratonic sequencing has been very expensive and difficult to explain. BioState solves these two problems through a dual approach: fundamental cost reduction and cutting-edge AI.

Technological innovation: Birt, Perd and Generative AI

The core of BioState products are two patented technologies: BIRT (Biostate Integrated RNASEQ technology) and PERD (probability expression reduction deconvolution). BIRT is a multiplexing scheme that allows RNA extraction from multiple samples and sequences simultaneously, reducing costs by almost ten times. Meanwhile, Perd applies novel algorithms to filter out “batch effects”, introduced due to differences in laboratory conditions or sample processing, which often mask biological signals in multi-site studies.

This highly standardized RNASEQ pipeline originates from Biostate’s proprietary basic model. BioenzymesIts function is very similar to the GPT model in natural language processing. Thousands of transcriptome profiles of tissue types, disease states, and species are trained, and biological enzymes capture the “biological grammar”, i.e., potential patterns of gene expression that define health and disease.

Just as GPT can be fine-tuned to write a paper or summarize legal documents, biological enzymes can also be adapted to detect early cancer recurrence, predict drug responses in autoimmune diseases, or stratify patients in cardiovascular trials. Biostate’s Prognosis AIbuilt on the top of the alkaloids, has shown hopes to predict leukemia recurrence and has been driven for multiple sclerosis through accelerated treatment programs.

“Just as Chatgpt changes language understanding by learning from trillions of words, we learn the molecular language of human diseases from billions of RNA expressions, explain Gopinaththe company’s chief technology officer. “What we are doing for molecular medicine, what are large language models doing for text – scaling the raw data so that the algorithm can eventually shine.”

Build the world’s largest RNASEQ dataset

To date, BioState has conducted over 10,000 samples for more than 150 collaborators, including Cornell and other major institutions. Its goal is to expand that number to hundreds of thousands of samples each year. Through its low-cost RNASEQ process and simplified data intake pipeline, OmicsWeb, the exponential growth is possible, which standardizes, tags and securely stores transcriptome data across jurisdictions.

The company’s cloud infrastructure includes several novel Genai tools, such as:

• OmicsWeb co-pilot – A natural language interface for analyzing codeless RNASEQ data.

• Quantaquill – An AI assistant, generates scientific manuscripts prepared for publication with numbers and citations.

• Embed surfers – A visualization tool that discovers hidden biological relationships in gene expression data.

Biostate has offices in Houston, Palo Alto, Bangalore and Shanghai, expanding globally to support a growing network of clinical and academic partners. The startup has been processing fresh and decades of tissue samples – Spiral Labs extracts insights from previously unusable specimens.

Universal AI for all diseases

Biostate’s final game is bold: Create a universal AI that can understand and guide treatments all Human disease. This unified approach contrasts with today’s fragmented biotech landscape, where each condition often requires its own isolated diagnostic tools and treatment pathways.

“ Rather than treating diagnosis and therapeutics as independent, isolated problems for each disease, we believe that modern and future AI can be versatile to understand and help cure each disease,” explain open.

By viewing biology as a generative system (the current molecular state determines tomorrow’s outcome), modern people believe that it can predict not only current health conditions, but also future disease trajectories and best interventions.

What’s next?

With over $20 million raised to date and a rapidly growing customer base, BioState is accelerating clinical collaboration in oncology, cardiovascular disease and immunology. The company’s next milestones include regulatory verification of its forecasting model and the commercial scale of its AI-driven diagnostic tools.

As Gopinath explain: “We’re not only explaining biology. We’re building biological equivalents with big language models, but this time it’s training for the human body.”

If BioState AI succeeds, the next wave of precision medicine may not only be reactive, it will be predictive, personalized and powered by generative AI.