Fout, Alex, authorByrd, Jonathon, authorShariat, Basir, authorBen-Hur, Asa, author2017-11-132017-11-132017https://hdl.handle.net/10217/184847This poster was presented at the 2017 Colorado State University Graduate Student Showcase, 9 November 2017.Determining the interface between two interacting proteins can help illuminate cellular biology, improve our understanding of disease, and aid pharmaceutical research. Such determination is expensive and time consuming using wet-lab experiments, which has motivated the development of computational methods. Inspired by the success of deep learning in image processing and other application areas, we adapt convolutional neural networks to work with irregularly structured data, such as proteins. We construct a novel pairwise classification architecture which is trained and tested with data from the Docking Benchmark Dataset versions 4.0 and 5.0. This outperforms the existing state-of-the-art prediction method, PAIRpred.born digitalStudent workspostersengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.proteinsprotein interfacemachine learningneural networksconvolutional neural networksgraph structured datagraphsdeep learningText