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A new computational model developed by Computation Institute scientists could help improve the allocation of U.S. biomedical research resources. The tool, called the Research Opportunity Index (ROI), measures disparities between resources dedicated to a disease and its relative burden on society. 

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Andrey Rzhetsky, professor of medicine and human genetics, isn’t a computer scientist by trade. But the messy complexity of biomedicine is a problem that fairly cries out for analysis by computation. It was also the perfect springboard for him to discuss the overarching theme in his work in his talk for the Visualization Speaker Series, “Adventures in Analysis of Large Biomedical Datasets”: getting data for complex networks, combining data sets, and drawing from them some “non-obvious conclusions.”

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Text mining is often discussed in the context of humanities research or marketing, where an enormous pool of text can be computationally sifted for new insight or targeted advertising.

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With the ebola scare of last year subsiding, scientists are now looking for ways to prevent or slow the next outbreak of the deadly virus.

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The back half of 2014 was filled with big announcements: partnerships with the White House and the City of Chicago, participation and leadership in some of the newest, biggest national scientific c

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The thesaurus is an essential tool for writers, a helpful reference when they need help expanding their vocabulary.

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The prevalence of autism and autism spectrum disorders in the United States nearly doubled between 2000 and 2008, prompting much debate about the driver of this startling increase. Because the cause of these developmental disabilities remains largely unknown, scientists have looked at both genetic and environmental factors, as well as changes in clinical diagnostic patterns, to explain why autism spectrum disorders appear to be on the rise. In a new study published in PLOS Computational Biology, a team led by CI senior fellow Andrey Rzhetsky brought 100 million medical records to bear on this problem, using computational techniques to reveal strong evidence of an environmental influence on autism prevalence.

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Although heavily studied, the specific genetic causes of “complex diseases,” a category of disorders which includes autism, diabetes and heart disease, are largely unknown due to byzantine genetic and environmental interactions.

Now, scientists from the University of Chicago have created one of the most expansive analyses to date of the genetic factors at play in complex diseases—by using diseases with known genetic causes to guide them. Analyzing more than 120 million patient records and identifying trends of co-occurrence among hundreds of diseases, they created a unique genetic map that has the potential to guide researchers and clinicians in diagnosing, identifying risk factors  for and someday developing therapies against complex diseases.