A team of computer engineering and bioengineering researchers from the University of Pittsburgh won the Best Paper Award at the 3rd International Conference on Machine Learning, Optimization & Big Data (MOD 2017). The paper titled, “Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models,” describes how automated machine reading can be used to pore over volumes of research and use that information to create models for understanding biological processes.
“These models are used to conduct and explain hundreds of thousands of simulated experiments, which would be impractical if done with biological material in the lab,” said Natasa Miskov-Zivanov, assistant professor of electrical and computer engineering at Pitt’s Swanson School of Engineering. “Our paper won the Best Paper Award because the methods it presents are critical to automating the process of model generation from vast amounts of literature without human intervention.”
Read more about the research at the Swanson School of Engineering's website.