Title: Predicting HIV resistance with the 3D neighborhood kernel
Authors: Schietgat, Leander ×
Lanssens, Ward
Fannes, Thomas
Ramon, Jan #
Issue Date: Sep-2016
Host Document: Proceedings of the 25th Belgian-Dutch Machine Learning Conference (Benelearn) pages:1-2
Conference: Annual Machine Learning Conference of Belgium and The Netherlands edition:25 location:Kortrijk, Belgium date:12-13 September 2016
Abstract: Recently, we developed the 3D Neighborhood Kernel (3DNK), which acts on 3D structures of small molecules and proteins. We showed its state-of-the-art performance on several biological datasets. However, 3D data are in many cases difficult to obtain. For this reason, we adopt a different strategy: instead of requiring actual 3D structures, we use as input protein sequences, of which we approximate the 3D structure through homology modelling. Then, we apply 3DNK on the approximated 3D protein structures and show that, on the task of predicting HIV resistance, we obtain better results than when using a kernel function based on the protein sequences alone.
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
Schietgatetal_3DNK_benelearn2016_final_version.pdf Accepted 225KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.