Nobel laureate Michael Levitt, PhD, has been using big data since before data was big. A professor of structural biology at Stanford, Levitt’s simulations of protein structure and movement have tapped the most computing power he could access in his decades-long career.
Despite massive advances in technology, key challenges remain when using data to answer fundamental biological questions, Levitt told attendees of the second day of the Big Data in Biomedicine conference. It’s hard to translate gigabytes of data capturing a specific biological problem into a form that appeals to non-scientists. And even today’s supercomputers lack the ability to process information on the behavior of all atoms on Earth, Levitt pointed out.
Levitt’s address followed a panel discussion on computation and crowdsourcing, featuring computer-science specialists who are developing new ways to use computers to tackle biomedical challenges.
Kunle Olukotun, PhD, a Stanford professor of electrical engineering and computer science, had advice for biomedical scientists: Don’t waste your time on in-depth programming. Instead, harness the power of a domain specific language tailored to allow you to pursue your research goals efficiently.
Panelists Rhiju Das, PhD, assistant professor of biochemistry at Stanford, and Matthew Might, PhD, an associate professor of computer science at the University of Utah, have turned to the power of the crowd to solve problems. Das uses crowdsourcing to answer a universal problem (folding of RNA) and Might has used the crowd for a personal problem (his son’s rare genetic illness).
For Das, an online game called Eterna – and its players – have helped his team develop an algorithm that much more accurately predicts whether a sequence of RNA will fold correctly or not, a key step in developing treatments for diseases that use RNA such as HIV.
And for Might, crowdsourcing helped him discover other children who, like his son Bertrand, have an impaired NGLY1 gene. (His story is told in this New Yorker article.)
Panelist Eric Dishman, general manager of the Health and Life Sciences Group at Intel Corporation, offered conference attendees a reminder: Behind the technology lies a human. Heart rates, blood pressure and other biomarkers aren’t the only trends worth monitoring using technology, he said.
Behavioral traits also offer key insights into health, he explained. For example, his team has used location trackers to see which rooms elderly people spend time in. When there are too many breaks in the bathroom, or the person spends most of the day in the bedroom, health-care workers can see something is off, he said.
Action from the rest of the conference, which concludes today, is available via live-streaming and this app. You can also follow conversation on Twitter by using the hashtag #bigdatamed.
Previously: On the move: Big Data in Biomedicine goes mobile with discussion on mHealth, Gamers: The new face of scientific research?, Half-century climb in computer’s competence colloquially captured by Nobelist Michael Levitt and Decoding proteins using your very own super computer
Photo of Michael Levitt by Saul Bromberger