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Robert F. Murphy, Ph.D.

AIMBE College of Fellows Class of 2005
For contributions to analytical cytometry and the automated analysis of sub-cellular-localization.

New CMU Degree Prepares Researchers for AI-Directed Experimentation

Via Carnegie Mellon University | October 9, 2018

Computers increasingly are helping scientists identify and select experiments necessary for scientific discoveries, so Carnegie Mellon University has created a two-year master’s degree program to train specialists needed to design, configure, operate and maintain these systems.

CMU’s Computational Biology Department will offer the Master of Science in Automated Science: Biological Experimentation beginning in fall 2019 and is accepting applications for its initial class through Dec. 1.

“Automation has disrupted numerous industries and is poised to radically transform the pace and economics of scientific research in academia and industry,” said Robert F. Murphy, head of the Computational Biology Department and co-director of the new master’s degree program. “We will train students to become leaders in this new field, where automated instruments and artificial intelligence combine to produce scientific discoveries… Continue reading.

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Robotically Driven System Could Reduce Cost of Drug Discovery

Via Carnegie Mellon | February 9, 2016

Researchers from Carnegie Mellon University have created the first robotically driven experimentation system to determine the effects of a large number of drugs on many proteins, reducing the number of necessary experiments by 70 percent.

The model, presented in the journal eLife, uses an approach that could lead to accurate predictions of the interactions between novel drugs and their targets, helping to reduce the cost of drug discovery.

To address this problem, the research team has previously described the application of a machine learning approach called “active learning.” This involves a computer repeatedly choosing which experiments to do, in order to learn efficiently from the patterns it observes in the data. The team is led by senior author Robert F. Murphy, professor and head of CMU’s Computational Biology Department.

“Our work has shown that doing a series of experiments under the control of a machine learner is feasible even when the set of outcomes is unknown. We also demonstrated the possibility of active learning when the robot is unable to follow a decision tree,” Murphy explained.

“The immediate challenge will be to use these methods to reduce the cost of achieving the goals of major, multi-site projects, such as The Cancer Genome Atlas, which aims to accelerate understanding of the molecular basis of cancer with genome analysis technologies.”

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