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LineaGen and IBM to Collaborate


By Maureen McDonough

July 20, 2005 | The University of Utah Health Sciences Center and LineaGen Research Corporation are teaming up with IBM to create a clinical genomics infrastructure and interface for the Utah Population Database and the Utah Genetic Reference Project database. The first phase of the collaboration will focus on integrating the collective 7 million population, demographic, and medical records so that they can be easily queried.

LineaGen’s key advantage is the ability to provide commercial access to the information in the Utah databases and associated biological samples. But unlike other organizations with information assets, LineaGen does not provide a subscription service. This would be impossible given the confidential nature of patients’ medical records. Rather, LineaGen acts as a single point of service for partners, sponsoring research to be conducted at the University of Utah.

“This is really unlike any kind of data set that IBM has seen before,” says Michael Paul, LineaGen’s chief operating officer (see “Medicine Gets Personal Touch,” page 21). While IBM has collaborated with the Mayo Clinic on a project similar in scale, it had dealt solely with clinical information. The Utah databases include population, familial, phenotypic, and genetic records.

“We chose to go with IBM because they have a core strategic focus in clinical genomics,” Paul says. “They are the thought leader in the field.”

LineaGen was founded as a nonprofit organization by the University of Utah in 2002 and was charged with three hefty tasks. They were to continue the development of the database and biological repository assets, sponsor medical research at the University of Utah, and enhance the economic development of the Utah biotechnology industry.

Paul believes that IBM’s Healthcare and Life Sciences Clinical Genomics Solution will help LineaGen meet all three of its goals. The system’s architecture will improve the database structures, the interface will allow clinical investigators to ask the right questions up front, and the data integration will increase the likelihood that novel biomarkers will be discovered, Paul says.

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