Scholarly snowball: Deep learning paper generates big online collaboration

Bioinformatics professors Anthony Gitter and Casey Greene set out in summer 2016 to write a paper about biomedical applications for deep learning, a hot new artificial intelligence field striving to mimic the neural networks of the human brain. They completed the paper, but also triggered an intriguing case of academic crowdsourcing. Today, the paper has been massively revised with the help of more than 40 online collaborators, most of whom contributed enough ideas to become co-authors. Gitter, of the Morgridge Institute for Research and University of Wisconsin-Madison; and Greene, of the University of Pennsylvania; both work in the application of computational tools to solve big challenges in health and biology. They wanted to see where deep learning was making a difference and where the untapped potential lies in the biomedical world.