Sunday, 21 December 2014

Big Data in medical records and test results

A new University of California Los Angeles (UCLA) institute is to help medical and biology researchers make sense of 'big data'.

""It's figuring out the signals from the noise," said UCLA Division of Life Sciences Dean Victoria Sork, who said that the university had invested $50 million altogether in the field of computational biosciences so far, hiring new faculty and improving facilities.

"UCLA has all these experts, but we were lacking the people and thinkers to say, how do we develop the tools to make the discoveries?" she said."

"The trouble with big data, [Professor Alexander] Hoffmann [head of the institute] said, isn’t volume. It’s complexity.

“I may have genetic data, or imaging data. I may have sleep-pattern data, or exercise data. I may have data on what people eat," he said. "Figuring out how the pieces go together takes computer science.""

Complexity certainly is a challenge. I doubt there is enough data to really make sense of it and what sense is made relies on assumptions about how we think genetic codes work (which we are starting to realise are a whole lot more complex than we thought they would be if we just sequenced everything). However, there is big money in big data and the promise of incredible insights. What hypotheses might we be chasing with the findings of this type of analysis?

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