Biostate AI Emerges from Stealth with Innovative RNA Sequencing Services

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Key Highlights

  • Launches Total RNA sequencing with BIRT technology for comprehensive RNA analysis.
  • Introduces OmicsWeb Copilot for advanced RNAseq data analysis and visualization.
  • Collaborates with Twist Bioscience and licenses IP from Caltech.
  • Raised over $4M in venture funding led by Matter Venture Partners.
  • Offers free Copilot platform to academic and nonprofit users.

Source: Business Wire

Notable Quotes

  • “The successful training of any AI well requires large quantities of relevant and high-quality data. Biostate AI has developed the instrumental technologies to facilitate the collection of more biological data at lower costs.”  David Zhang, Co-founder and CEO at Biostate AI
  • “Bioinformatic analysis of RNAseq and other omics data is a highly complex, multi-step process that currently takes many hours of dedicated specialized programming.”  Ashwin Gopinath, Co-founder and CTO at Biostate AI
  • “AI is the next frontier and AI needs data, and biological data is a lot harder to get than text or images. We are excited about the potential for Biostate’s technology to dramatically lower the cost of collecting RNAseq datasets.”  Haomiao Huang, Founding Partner at Matter Venture Partners

SoHC's Take

Biostate AI’s emergence from stealth with its innovative RNA sequencing services marks a significant advancement in the field of bioinformatics and AI. The introduction of Total RNA sequencing and OmicsWeb Copilot addresses critical challenges in RNAseq data analysis, offering comprehensive, scalable, and cost-effective solutions. By partnering with prominent institutions like Twist Bioscience and Caltech, Biostate AI is poised to make a substantial impact on scientific discovery and AI training. Their commitment to providing free tools to academic and nonprofit researchers further demonstrates their dedication to advancing the field and fostering collaborative innovation. This launch signifies a pivotal step toward harnessing the full potential of multiomic data for health predictions and drug efficacy assessments.

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