The State University of New York at Buffalo is using an IBM Netezza appliance and Revolution Analytics software to create algorithms to accelerate multiple sclerosis research.
The State University of New York (SUNY) at Buffalo is developing algorithms
using an IBM Netezza 1000 data warehouse appliance and Revolution Analytics
software to further multiple sclerosis (MS) research.
The algorithms will allow the university to study genomic data sets for more
than 2,000 genetic and environmental factors that may lead to MS symptoms. IBM
revealed details about the genomic research project on April 26.
Personalized
medicine using big data is a major trend in health care, and the IBM
hardware and Revolution R Enterprise software allow researchers to develop
individual treatment to slow MS symptoms such as brain injury, physical
disability and cognitive impairments, IBM reported.
Researchers will examine how factors such as gender, geography, ethnicity,
diet, exercise and sun exposure may lead to MS. Living and working conditions
may also lead to the disease, IBM reported.
"My group has been interested in developing new algorithms and new
methods for gene environment interactions," Dr. Murali Ramanathan, lead
researcher at SUNY Buffalo, told
eWEEK. "We have used Netezza
appliances along with Revolution R capabilities to speed up and enhance the
performance of our gene environment interaction and analysis algorithms."
Before using the Netezza appliance, analysis of genetic material would take
several days, and now it takes only a few minutes, said Ramanathan.
"We were able to do thousands of permutations within the Netezza box,
something we were not able to do in our earlier implementation," said
Ramanathan. The increased speed and performance allows researchers to explore
additional questions on the role of genetics and environment in leading to the
onset of MS.
In addition, the Revolution R software allows researchers to streamline
their workflow to handle a huge amount of the genetic data, according to
Ramanathan.
"In the past, the SUNY team would have had to rewrite the entire
algorithm, which would have required a great deal of time from a grad student
or Ph.D. candidate," David Smith, vice president of marketing and
community for Revolution Analytics, told
eWEEK in an email.
Using Revolution R Enterprise for IBM Netezza, SUNY Buffalo researchers were
able to reduce computation time from 27.2 hours without the Netezza device
to 11.7 minutes with it, said Smith, who wrote about the project in a
blog
post.
"With Revolution R Enterprise for Netezza, the R language is embedded
in each of the processing cores of the IBM Netezza 1000 appliance," said
Smith. "This not only enables massively parallel computations using the R
language on the high-performance IBM hardware, it also means that data does not
have to be moved to another environment for processing, [therefore] further
increasing performance."
About 400,000 people in the United States have been diagnosed with MS,
according to the National Multiple Sclerosis Society.
IBM BladeCenter servers power the Netezza storage appliances that SUNY
Buffalo is using.
Although SUNY Buffalo has been working with Netezza appliances for two
years, the addition of Revolution R software brings added analytics
capabilities to study more interactions between genetic material and
environmental factors, which are called phenotypes. "Now they can do over
1,000 variables getting more of these other kinds of data," Shawn Dolley,
vice president of big data, health care and life sciences at IBM, told
eWEEK.
"[Groups] of MS patients with phenotypes in common are what lead to
more narrowed interventions," said Dolley.
The goal of the research is to not only develop a cure for the disease but
to also help MS patients have quality of life, Dolley noted.
"The technology and the algorithms helped us address risk and
progression," Ramanathan added.
"The collaborative environment and the new algorithms and architectures
have been very productive, and our hope is that we'll be able to leverage these
new algorithms and data sets for developing a cure and prevention for MS,"
said Ramanathan."That's our hope and mission."
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