SOLVING THE MAJOR CHALLENGES OF DATA INTEGRATION AND ANALYTICS FOR TRANSLATIONAL MEDICINE APPLICATIONS

Solving the major challenges of data integration and analytics for translational medicine applications

Learn how to accelerate your research abilities in life sciences and find out how to use virtualized data to drive discoveries and breakthroughs

Translational medicine is an area of research in life sciences that focuses on bringing together early-stage research and development with downstream clinical outcomes to better understand the effects of drugs and therapies for real-world patient outcomes. This area has been considered a ‘holy grail’ of life sciences for decades because solving it effectively means clinical delivery of new medicines from laboratories will be more easily achievable.

To accomplish these outcomes, it is important to be able to utilize a plethora of data from different research and clinical areas. These data sources are normally spread across organizations, exist in incompatible formats and are often inconsistently labeled. This means a wide variety of data sources are required for translational medicine to be effective.

Traditionally, software development in clinical research meant that large amounts of heterogeneous data needed to be migrated together, transformed and mapped into a common repository so complex searches and analytics can be performed.

The first stage of the data migration process normally includes building a pipeline for the data to be extracted, transformed and loaded (ETL’d) into the new system – a process which can take months, or even years, to complete.

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Once built, these pipelines are often rather rigid and brittle, in that they connect data together in specific ways for specific purposes. So, when the research questions change, often the ETL pipeline must be rebuilt or extended over and over. It is hard to have a flexible and ad hoc type of system that can keep up with the scientific demands of translational medicine where new topics and patterns of interest are constantly being thought of as potential research avenues.

At Leap Analysis (LA), we have built a new type of technology for use in translational medicine, one that allows for ad hoc queries and analytics to be run by scientists directly. Using LA, researchers can focus on medical outcomes rather than data wrangling. By removing the need for ETL pipelines and the need to move or copy the data, LA provides a straightforward means to access data directly from the source via intelligent data connectors.

These connectors are driven by semantic metadata (data models representing the most basic classes, attributes and relationships in the data) and machine learning (which scans, reads and presents the original data schemas directly to LA’s engine automatically).

Users can connect directly to data sources, via a cloud-based portal, regardless of their physical location. LA provides nearly immediate access to a wide variety of sources and formats dramatically reducing the time needed for other search and analytics systems that require the expense of building data pipelines and complicated mapping strategies.

LA is changing the way that translational medicine gets done by:

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