single marker was not fruitful. Many mutations combine with external factors (epigenetics) to initiate MS. To tease out the relevant factors, the SUNY team developed AMBIENCE to find linear and nonlinear correlations between various factors. Initially, the computational problem seemed too complex, requiring evaluation of 1018
possible correlations.
Using IBM’s PureData™ System for Analytics powered by Netezza® technology (parallel processors) and R from Revolution Analytics (Mountain View, CA), ultimately the compute time required was reduced from 27 hr to 11.7 min. Even better: non-IT scientists can write the query to test a new hypothesis.
Another example: The medical school at Vanderbilt University (Nashville, TN) found that IBM’s help was crucial to the success of a project that examined de-identified medical re- cords of 2.2 million patients collected over the last 20 years. The database, called the Synthetic Derivative, consists of both numerical and text. It is an information-rich source that elucidates disease patterns and guides therapy. When combined with a separate genetic database (BioVU), it is possible to explore phenotypical factors. Initially, responses to queries required more than six months. However, with IBM’s PureData System using Netezza technology, response time is less than a minute. For infor- matics this is noteworthy since it demonstrates successful integration of two large databases with different structure and content, including natural language. For the researcher, it is now practical to quickly test hypotheses while the idea is fresh.
Still another example is IBM’s co-sponsoring and organizing the sbv IMPROVER Challenge series, where sbv stands for systems biol- ogy verification. This series has stimulated labs around the globe to hone their skills develop- ing informatics tools to elucidate biochemical functions.1,2
Data discovery When you already know what you’re looking for
in your data, you often put on the blinders and don’t focus on what you could learn. But if you want to find what you don’t know and test new hypotheses against existing data sources, this is what is referred to as “data discovery,” a concept
that is extremely relevant to the life sciences. However, comparing results from data sources is often problematic due to varying data struc- tures. A lecture by Dave Anstey of YarcData (a Cray company, Pleasanton, CA) described advanced computing hardware that facilitates data discovery using semantic technology sup- ported by massively parallel processing.
In more detail, semantic technology with data in the RDF (Resource Description Framework) model has many advantages over relational data models. Because RDF represents data as graphs—nodes of information connected by named links—the model enables you to merge data more easily and provides additional con- text to the data. Graphs of considerable size are difficult to analyze without semantic technol- ogy because as nonpartitionable, dynamic structures, they require a large memory and rapid processing power.
YarcData addressed these unique needs of semantic technology by introducing the Urika™ appliance with up to 512 TB of shared memory and Cray’s Threadstorm™ massively multithreaded processors. The Urika appliance is designed to handle billions of triples in memory, starting at the low end with 8–12 bil- lion and increasing exponentially from there, which greatly accelerates processing.
One benchmark study at Sandia National Laboratories showed that a 32-processor Urika appliance required only 30 sec in execution time for a multithreaded implementation, com- pared to 10.8 hr using a 48-processor traditional system after months of optimization.
For those concerned about transitioning from SQL to NoSQL technology, Mr. Anstey pointed out that YarcData partner IO Informatics (Berkeley, CA) markets Data Manager, which can quickly convert RDB to RDF and back. Thus, you can perform searches across numerous databases, and then convert back to relational formats, such as Excel, for more traditional data storage and presentation.
Innovative technologies that facilitate data discovery are the way of the future, as YarcData declared that the data discovery market seg- ment had revenue of approximately one billion dollars in 2013, with a growth rate of over 30% per year.
AMERICAN LABORATORY • 35 • JUNE/JULY 2014
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