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Personalized medicine with Northrop Grumman and Google Cloud Platform

Currently, the way that doctors and clinicians approach medical treatment is to look at a patient’s symptoms, determine a prognosis, and assign the appropriate treatment. While sensible, this reactive approach leaves a lot open for interpretation and may not hone in on critical clues such as predisposition to genetic mutation or length of time an illness lingered before symptoms appeared. With added insights about genetic makeup, environment, socioeconomic factors and family medical history, doctors and clinicians gain the ability to better tailor and individualize medical treatment.

Doctors need new technologies in order to provide this individualized care. Researchers devoted to personalized medicine can now use big data tools to analyze clinical records, genomic sequences, and laboratory data. All of this valuable data may reveal how differences in an individual’s genetics, lifestyle, and environment influence reactions to disease. And ultimately, it may show us that customized treatments can improve outcomes. To get there, we first need to overcome the challenge of data inundation. Vast health datasets create significant impediments to storage, computation, analysis, and data visualization. The raw information for a single human genome is over 100 GB spanning over 20,000 genes, and the doctors’ handwritten notes are hard for computers (and people) to make sense of. There just aren’t enough tools and data scientists available to leverage large scale health data.

At Northrop Grumman, we’ve prototyped a personalized health analytics platform, using Google Cloud Platform and Google Genomics, to improve knowledge extraction from health data and facilitate personalized medicine research. With our personalized health analytics platform, a genomics researcher would be able to evaluate diseases across a set of patients with genomic and health information. In the past, a simple question about what genetics are linked to a medical condition might take hours, or even days, to execute. By leveraging Google Cloud Platform, in combination with our own algorithms, the analysis of 1,000 patients’ genomic data, across 218 diseases, generates near real-time results.

Northrop Grumman’s analytics platform would provide multiple benefits to researchers. With Google Genomics and Google BigQuery, terabytes of genomics information can be analyzed in only a few seconds, so researchers would see faster research results. This increase in the speed of discovery deepens our understanding of how genetic variations contribute to health and disease. In addition, the scalable storage and analysis tools provided by Google Cloud Platform and Google Genomics reduce costs and increase security when compared against in-house IT systems. And lastly, our platform aims to improve patient health by expanding the knowledge base for personalized medicine with discovery of complex hidden patterns across long time periods and among large study populations.

The Architecture

To make personalized medicine research easier, we architected our health analytics platform in layers. Here they are starting from the base layer, progressing upward:

  1. Massive Data Storage: A storage layer leverages Google Genomics to efficiently store and access genomic data on the petabyte scale and Northrop Grumman knowledge engines and framework to efficiently process and store electronic health records (EHR) data.
  2. Annotation Layer: The annotation layer provides tools to extract clinical knowledge from structured and unstructured EHR data sources. It also includes a database containing aggregated phenotypic and disease associations from public sources. These enable improved functional annotation of the genomic data.
  3. Analytics Layer: The analytics layer is built on top of Google BigQuery and Google Compute Engine to provide high-performance modeling and analytics tools. With these, we can demonstrate genomic risk modeling with analysis time scales of only several seconds.
  4. Visualization & Collaboration Layer: The visualization and collaboration layer provides a framework for high-level analytics, visualization, and collaboration tools.
The system architecture for Northrop Grumman’s personalized health analytics platform. A layered approach is designed to provide an integrated research environment with greater access to storage infrastructure, improved information extraction and annotation tools, more powerful computational platforms and improved collaboration and visualization tools. 

New Breakthroughs in Personalized Medicine

Today our personalized health analytics platform is a prototype, but the results are promising. Our health analytics platform may improve a researchers’ speed of discovery, lower the costs of storing massive amounts of health data, offer better security than in-house IT systems and ultimately lead to breakthroughs in personalized medicine and treatment. If you’re interested in learning more, please contact Northrop Grumman.

– Posted by Leon Li, Future Technical Leader and Systems Engineer at Northrop Grumman Corporation

Feed Source: Google Cloud Platform Blog
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