Cloud computing speeds up biomedical data analysis

We came across this story in Medical Technology Business Europe

In summary:

Biomedical informatics scientists at The Ohio State University have created a system that uses cloud computing to enable researchers around the world to access and analyze biomedical data.

Funding has been provided by the National Institute of Health and the creators of the TRIAD system are making some pretty big claims:

“With the current technology, a researcher might dedicate more than 100 hours to connect the dots between a set of tissue samples, the individual medical histories for the patients who provided those tissues, and then analyzing the group as a whole. With the TRIAD platform, researchers can now execute this type of search and analysis in minutes,” says Philip RO Payne, chair of the department of biomedical informatics at The Ohio State University Medical Center.

Having been one of those researchers, I have reservations – in that the issues we encountered on a weekly basis (trying to link tissue analysis outputs to clinical records and from that assess correlations) were less to do with processing power and more to do with conceptual and data entry issues.

The kind of issues we encountered were as banal as ‘when looking at Acute Leukaemia relapse scenarios, what constitutes a sample taken at relapse before treatment when we can only be confident that dates entered on the tissue storage and clinical data systems are accurate to around a fortnight and outcome assessment is carried out a fortnight after the start of treatment?’.

To be fair, the aims of the developers of TRIAD (which, incidentally uses caGRID technology for all those denigrators of caBIG) are directed at interoperability between data encoded using different standards:

“When it comes to biomedical research, you have the digital equivalent of the Tower of Babel. One piece is written in French. And another is written in Russian. And maybe a third component is in Chinese,” explains Payne. “TRIAD acts like the ultimate interpreter between all the different “languages” that biomedical data comes in so that researchers spend time figuring out how the information could improve the way we treat a disease rather than spend time finding and translating various data sets.”