Exai Bio, a next-generation oncRNA- and generative AI-based liquid biopsy company, today announced a peer-reviewed publication about its generative AI model in Nature Communications. The paper entitled “Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer” demonstrates that Exai’s generative AI model surpasses commonly used machine learning methods in overall lung cancer detection performance. Exai’s platform achieved higher overall sensitivity and specificity for cancer detection across all stages, beating the performance of other methods on held-out validation datasets by 30%.
“The publication of our study in a preeminent journal like Nature Communications demonstrates the power of Exai’s Bio’s generative AI platform and its ability to surpass the performance of other commonly used methods in liquid biopsy,” stated Dave Daly, Chief Executive Officer of Exai Bio. “Harnessing our unique advantages in generative AI as well as novel RNA biomarkers will be integral to our next-generation liquid biopsy tests that will truly deliver on the promise of early cancer detection.”
Congratulations to Hani Goodarzi, PhD, the UCSF founder!