Stanford systems-medicine chief Atul Butte, MD, PhD, is an intrepid data miner who firmly believes that analyzing vast reservoirs of public health information is the “fastest, least costly, most effective path to improving people’s health." His latest paper shows how he and colleagues combed through mountains of medical information to identify new links among genes, diseases and traits. My colleague Bruce Goldman summed up the findings in a previous Scope post:
... by cross-referencing voluminous genetic data implicating particular gene variants in particular diseases with equally voluminous data associating the same gene variants with other, easily measured traits typically considered harmless, Butte and his associates were able to pick out a number of such connections, which they then explored further by accessing anonymized electronic medical records from Stanford Hospital and Clinics, Columbia University, and Mount Sinai School of Medicine.
A recent entry on the NIH Director's Blog singled out Butte's findings as an example of how "Big Data may provide priceless raw material for the next era of biomedical research." Francis Collins, MD, PhD, director of the National Institutes of Health, writes:
What I find most noteworthy about this work is not the specific findings, but how the researchers demonstrate the feasibility of mining vast troves of existing data—genetic, phenotypic, and clinical—to test new hypotheses.
Indeed, we are at a point in history where Big Data should not intimidate, but inspire us. We are in the midst of a revolution that is transforming the way we do biomedical research. In some cases, rather than posing a question, designing experiments to answer that question, and then gathering data, we already have the needed data in hand—we just have to devise creative ways to sift through this mountain of data and make sense of it.
As a reminder, Butte and others from academia, industry and government are gathering here on May 21-23 for the Big Data in Biomedicine conference. Registration information can be found on the conference website.
Previously: Odd couples: Resemblances at molecular level connect diseases to unexpected, predictive traits, Nature/nurture study of type 2 diabetes risk unearths carrots as potential risk reducers, Mining medical discoveries from a mountain of ones and zeroes and Newly identified type-2 diabetes gene’s odds of being a false finding equal one in 1 followed by 19 zeroes
