Repository logo
 

Protein interface prediction using graph convolutional networks

Simple item page
dc.contributor.authorFout, Alex, author
dc.contributor.authorByrd, Jonathon, author
dc.contributor.authorShariat, Basir, author
dc.contributor.authorBen-Hur, Asa, author
dc.date.accessioned2017-11-13T15:18:06Z
dc.date.available2017-11-13T15:18:06Z
dc.date.issued2017
dc.descriptionThis poster was presented at the 2017 Colorado State University Graduate Student Showcase, 9 November 2017.en_US
dc.description.abstractDetermining 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.en_US
dc.description.awardGraduate Student Council - New Graduate Student - Research Top Scholar.
dc.format.mediumborn digital
dc.format.mediumStudent works
dc.format.mediumposters
dc.identifier.urihttps://hdl.handle.net/10217/184847
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartof2017 Projects
dc.rightsCopyright 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.
dc.subjectproteins
dc.subjectprotein interface
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectconvolutional neural networks
dc.subjectgraph structured data
dc.subjectgraphs
dc.subjectdeep learning
dc.titleProtein interface prediction using graph convolutional networksen_US
dc.title.alternative105 - Alex M Fouten_US
dc.typeText
dc.typeImage
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
protein-interface-prediction-CSU-poster.pdf
Size:
5.18 MB
Format:
Adobe Portable Document Format
Description:
PDF Poster
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.05 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

2017 Projects