Substantial data analysis improves gene-environmental correlation identification to help develop new treatment for multiple sclerosis
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- IBM® PureData™ System for Analytics
- IBM Business Partner Revolution Analytics
Tech Stack
- Data Analytics
- Data Warehousing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Innovation Output
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Data Mining
Applicable Industries
- Education
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
Founded in 1846, the State University of New York (SUNY) at Buffalo is the flagship institution in the State University of New York system. It is the largest and most comprehensive campus in the 64-campus SUNY system and home to one of the leading multiple sclerosis (MS) research centers in the world. University at Buffalo offers more than 100 undergraduate degrees, 205 master’s degrees, 84 doctoral degrees and 10 professional degrees.
The Challenge
As one of the leading multiple sclerosis (MS) research centers in the world, researchers at University at Buffalo wanted to identify and understand environmental factors that may contribute to MS. However, gene-environmental research presented researchers with enormous volumes of data for which they needed high-performance processing power and speed to make meaningful, publishable discoveries. A major challenge in gene interaction research is analyzing the explosions of immense data sets at a speed that will help save lives.
The Solution
University at Buffalo researchers use a powerful solution that combines data warehouse and analytic capabilities to handle the exponential growth rate of genetic variations involved in breakthrough data-mining methods for gene interaction in disease discovery for MS. The solution helps enable researchers to use new algorithms and analyze volumes of data that could number in the quintillions, which was previously impossible, allowing them to examine more than 2,000 genetic and environmental factors that may contribute to the development and progression of MS. For example, researchers can analyze vitamin D metabolites’ protective associations with disability and brain atrophy in MS and its possible correlation to why MS is more common in northern latitudes and less common toward the equator. And in turn, new insights can help develop therapeutic and prevention strategies for treating and managing MS.
Operational Impact
Quantitative Benefit
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