It’s basic biology: How data visualization is improving translational medicine

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For researchers and practitioners of translational and personalized medicine, implementing effective diagnosis and treatment methods is all about bringing together novel and diverse data and being able to draw meaningful insights from that information.

It’s been well established that not all patients will respond the same way to the same drugs, but the challenge is figuring out exactly which factors impact a drug’s efficacy within an individual’s body. The only way to flush out patterns and correlations among patient populations is to compare mass amounts of diverse data. Even after carrying out data reductions, all these data sets are still different types of data – the solution is finding a tool that is capable of bringing all of this information together and integrating it in a coordinated fashion.

The end goal is that from this data integration, insights can then be revealed. While software programs can perform the heavy lifting parts of data crunching and analysis, it still requires a human operator to explore and identify potential areas of correlation. Data mining of patient populations cannot be done without a scientist first asking: how, why, what, when, who?

Biologically, humans are well wired for exceptional visual analysis. Our optic nerve can transfer incoming visual data to our brains as fast as an Ethernet connection. Once received in the brain, evolution has honed our abilities to distinguish shapes, edges, and boundaries, and to identify patterns from that visual data – all almost instantaneously.

When it comes to working with data sets, it’s our pattern matching ability that makes data visualization such an effective analysis tool. As data is portrayed visually, we can quickly distinguish differences in shapes and patterns between sets much more quickly than we would be able to do by looking at numbers alone.  Visualization gives meaning and context to data that would otherwise be vast compilations of numbers, facts and measurements.

For many scientists in the clinical and life sciences market, the TIBCO Spotfire® data visualization software platform has already been a helpful tool for understanding data. Many users have already implemented their own best practices strategies for using visual analysis tactics in data mining. However, through our exclusive agreement with TIBCO, Inc., our Informatics team is now focused on developing software templates and add-ons that will cater specifically to the life sciences industry.

Over the next several years, as data visualization becomes a best practices standard for analysis of data in clinical and life sciences research, one of the biggest changes we can hope to see will be the advent of the application of next generation sequencing in the clinic. When it comes to understanding diseases like cancer, scientists will be able to diagnose types of cancer instead of locations of cancer, narrowing down treatment options that are most likely to be effective against a certain cancer’s type.  Visualization software will be used return better diagnoses, to recommend more specific therapies, and to even provide guidance for illness prevention.

To learn more about data visualization and Spotfire, consider attending our Dr. Spotfire Day on May 6in San Jose, California.