Dante Genomics, a global provider in genomics and precision medicine, announced a strategic AI collaboration with Amazon Web Services (AWS) to bring generative AI to the clinical care of patients with genomic diseases.
Dante Genomics’ early focus on whole genome sequencing provides the company with a natural competitive advantage to build large language models (LLMs) for clinical genomics. Each genome has 10,000 times more data than a traditional genetic panel and more than 20 times more data than whole exome sequencing.
“While companies were debating whether the exome was too much data, we were laser focused on whole genome sequencing,” says Mattia Capulli, PhD, co-founder and chief scientific officer of Dante Genomics. “It was not easy, but the dedicated time and strategic investments are paying off, giving us an amazing source of data to advance LLMs for clinical genomics.”
Dante Genomics utilizes the large language model (LLM) Amazon Bedrock on the AWS platform to enable Dante customers to better access and navigate its broad and growing collection of more than 130 genomic reports with genomic applications across clinical areas, including proactive screening, longevity, and personalized medicine.
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“For more than seven years, Dante Genomics has been steadfast in its mission to deliver whole genome sequencing at scale, and with an ever-growing database, we are unmatched in our expertise in the clinical utility of the genome,” says Andrea Riposati, co-founder and CEO of Dante Genomics. “By partnering with the tech experts at AWS, Amazon Bedrock as a platform will help us make great strides in revolutionizing the everyday application of genomic data in personalized medicine, delivering better health outcomes with genomic data as a foundation.”
In this collaboration, Dante Genomics aims to allow a subset of phenotypes such as eye color, ancestry and monogenic disorders, such as cardiomyopathies, respiratory conditions and seizures, to be explored using a chat-style interface.
This use case for LLMs in genomic medicine is based on extensive research into training honest and responsible AI systems, according to the company. The system will relay the outcome of genetic inference to the patient, allow the patient to easily distinguish comorbidities and request additional information, and accurately identify when a physician’s involvement is necessary.
