FLICKR, MOONLIGHTBULBThe odds of a scientist becoming an academic principal investigator (PI) can be predicted with publication data, according to Lucas Carey from Spain’s Pompeu Fabra University and his colleagues. The team developed an online tool, dubbed PIPredictor, which uses a machine-learning approach to analyze a user’s PubMed data and that has already churned out more than 800 career-success estimates to date. Carey and his colleagues describe their tool in Current Biology today (June 2).
“We show that becoming a research professor is highly predictable, [and] we analyze the features that are predictive” of success, Carey told The Scientist in an e-mail. The algorithm can predict who might become a PI and how long it could take for them to do so with an area under the curve (AUC), a measurement of accuracy, of 0.83 and 0.38, respectively.
While he was a postdoc in Eran Segal’s lab at the Weizmann Institute of Science in Rehovot, Israel, Carey and two then-PhD students, David van Dijk and Ohad Manor, decided to apply the machine learning-based approaches they were using to predict molecular mechanisms from gene expression data to guess who among them might one day become a PI. “There was quite a ...
