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Google teams up with Stanford Medicine for Clinical Genomics innovation

August 8, 2016
Sam Schillace

VP of Engineering, Industry Solutions

Google Cloud Platform has teamed up with Stanford Medicine to help clinicians and scientists securely store and analyze massive genomic datasets with the ultimate goal of transforming patient care and medical research.


Stanford Medicine ranks as one of the country’s best academic medical centers, and we’re eager to see what can happen when we work together. We anticipate that our contributions of HIPAA-compliant cloud computing, machine learning and data science — combined with Stanford’s expertise in genomics and healthcare — could lead to important advances in precision health, a predictive and preventive approach to healthcare.


This is a great opportunity to bring data science to patient care by combining genomics and traditional health records. Our collaboration is in support of the new Clinical Genomics Service at Stanford Health Care, which aims to sequence and analyze thousands of patients’ genomes. Cloud Platform will allow Stanford scientists and clinicians to securely analyze these massive datasets immediately and scale up painlessly as clinical genomics becomes more commonplace.

As genome sequencing becomes affordable, more and more patients will be able to benefit from it. Modern cloud technology and data science tools can vastly improve analysis methods for genomic data. Working with the team at Stanford, we expect to build a new generation of platforms and tools that will facilitate genome analysis at massive scale, providing actionable answers about gene variants from each person’s genome in a fraction of the time it takes now, and use that information to make better medical decisions.

Stanford researchers already have some cool ideas in mind for expanding beyond genome data, such as using machine-learning techniques to train computers to read pathology or X-ray images and identify tumors or other medical problems. They’ve also amassed years of anonymized patient data that could be used to teach algorithms to distinguish false signals from real ones, such as hospital alarms that go off when nothing is wrong with a patient.

Together, we believe these efforts will pay off in new insights into human health and better care for patients at Stanford and other institutions.

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