Biostate AI, an innovator combining artificial intelligence with RNA sequencing, has secured $12 million in Series A funding led by Accel, with participation from Gaingels, Mana Ventures, InfoEdge Ventures, and existing investors Matter Venture Partners, Vision Plus Capital, and Catapult Ventures. This investment brings the company's total funding to over $20 million.

The new capital will accelerate Biostate AI's mission to make precision medicine more accessible and affordable, starting with expanding RNA sequencing (RNAseq) services for molecular research across the United States. Founded by former professors and serial entrepreneurs David Zhang (CEO) and Ashwin Gopinath (CTO), the company believes the RNA transcriptome represents an underutilized real-time biomarker for human health.

Biostate AI has developed proprietary technologies called BIRT and PERD that significantly reduce the cost of processing tissue samples for RNA sequencing, working effectively on both fresh and decades-old tissues. This cost reduction allows researchers to process 2-3 times more samples within existing budgets.

"Rather than solve the diagnostics and therapeutics as separate, siloed problems for each disease, we believe that the modern and future AI can be general purpose to understand and help cure every disease," said David Zhang, co-founder and CEO of Biostate AI, and former Associate Professor of Bioengineering at Rice University. "Every diagnostic I've built was about moving the answer closer to the patient. Biostate takes the biggest leap yet by making the whole transcriptome affordable."

The company addresses three critical limitations in traditional RNA sequencing: high costs, data aggregation challenges, and lack of standardization. By lowering internal costs, Biostate AI can collect millions of consented, de-identified RNAseq profiles globally, creating a substantial dataset for training sophisticated generative AI models. Their unified workflow standardizes experiments, allowing their AI to consistently learn biological patterns without confounding batch effects.

Unlike Large Language Models that learn from text, Biostate's AI models identify gene expression signatures correlated with specific disease states and treatment responses. This enables detection of subtle molecular changes that may precede clinical symptoms by weeks, months, or even years, potentially enabling earlier interventions.

The company has shown early proof-of-concept success in predicting disease recurrence in leukemia patients and plans to expand collaborations with clinical partners in oncology, autoimmune disease, and cardiovascular disease. Since launching its commercial offering two quarters ago, Biostate has processed RNAseq for over 10,000 samples from more than 150 collaborators and customers at leading institutions, including pilot projects with Cornell for leukemia and with the Accelerated Cure Project for multiple sclerosis.

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