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Precision Medicine Step 2: Store and Analyze Data

Precision Medicine Step 2: Store and Analyze Data

Johns Hopkins Medicine has recognized the Secure Analytics Framework Environment (SAFE) and Precision Medicine Analytics Platform (PMAP) computing platforms as the best practice environments for research. 

SAFE 

Researchers can now save, access and analyze their data remotely in the SAFE. Combining a virtual desktop with a storage network, the SAFE allows Johns Hopkins Medicine researchers to retrieve data from any mobile device or computer using a secure wireless connection and JHED ID. The SAFE is considered the best storage location for research studies as well as the best place to review them.

The platform also comes with Johns Hopkins IT-approved collaboration and statistical analysis tools, including:

  • Microsoft Office Word and Excel
  • SAS/STAT – a common statistical package that includes more than 100 prewritten analysis procedures
  • STATA – an analytics package that allows researchers to manipulate and visualize statistics and easily produce reports
  • R and R Studio – a software programming language and a software environment that investigators can use for statistical computing and graphics
  • Python – a high-level, general-purpose programming language that is easy for novice and experienced coders to use

PMAP 

Because users access it through the SAFE, the IRB and Data Trust recognize PMAP as the most secure place for researchers to store and conduct complex analyses. In PMAP, researchers can use the system’s most sophisticated data analysis tools. They include natural language processing, which is a programming technique that finds and extracts data of interest from open text documents, such as provider progress notes or MyChart messages between patients and providers.

What also differentiates PMAP from other platforms is its ability to support learning from patterns identified in the data. Programs such as Jupyter Notebook, Docker and Compute can be used to apply machine learning principles that allow researchers to discover new insights in their data. Researchers can also share code, equations and data charts. These tools can be used to develop algorithms, create calculators that can predict disease risk, and offer recommendations about best treatment and medication practices.

 
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