Genomics & Data Science: A Conversation with Mark Gerstein

A look at biomedical data science with Mark Gerstein, Albert L. Williams Professor of Biomedical Informatics and professor of molecular biophysics and biochemistry, and of computer Science. Co-director of the Yale Program in Computational Biology & Bioinformatics. Gerstein will describe the framework of genomics, which is one of the leading sub-fields under the data science umbrella.

This talk is part of the Convening Yale series. Convening Yale presents talks by faculty and leaders from throughout Yale University, who share their research and expertise and help students broaden their understanding of an increasingly complex world. The Convening Yale series is made possible through the generous support of the Robert J. Silver ’50 Fund for Innovation in Management Education.

This event is open to the Yale community. Registration is required.

On the surface, scholars of genomics and practitioners of business may not have much in common, but they face the same 21st-century concern: how to navigate the ethical dilemmas presented by big data, says Mark Gerstein, Albert L. Williams Professor of Biomedical Informatics and professor of molecular biophysics and biochemistry, of statistics and data science and of computer science.

Gerstein spoke to students at the Yale School of Management on April 23 as part of the Convening Yale lecture series, which brings scholars across disciplines to campus to discuss their research.

“The path forward in genomics, just as in the path forward for Google and Facebook, is in large-scale sharing,” said Gerstein. “Mathematically we can show that we get more statistical power to find things out with bigger data sets, and because of that there’s a compelling argument in medical research that we should amass gargantuan amounts of information.”

But in dealing with such data sets, whether in medicine or in business, concerns over eroding individuals’ privacy, as well as how to ensure that results are unbiased, will invariably arise, said Gerstein. There is not an obvious solution to these problems, though Gerstein advocates for hybrid approaches integrating technical fixes with new social constructs.

Yale SOM students will likely work with big data in their careers, Gerstein said, and should be learning some relevant foundational ideas and skills:

• The ability to program, deal with a large amount of data, and make “practical and interpretable decisions from it.”
• An understanding of “scaling and the volume of data and how exponential scaling changes the data.”
• An understanding of “networked science and the way you can take cross-cutting ideas and apply them to other disciplines.”