Abstract
Kernels for structured data have gained a lot of attention in a world with an ever increasing amount of complex data, generated from domains such as biology, chemistry, or engineering. However, while many applications involve spatial aspects, up to now only few kernel methods have been designed to take 3D information into account. We introduce a novel kernel called the 3D Neighborhood Kernel. As a first step, we focus on 3D structures of proteins and ligands, in which the atoms are represented as points in 3D space. By comparing the Euclidean distances between selected sets of atoms, the kernel can select spatial features that are important for determining functions of proteins or interactions with other molecules. We evaluate the kernel on a number of benchmark datasets and show that it obtains a competitive performance w.r.t. the state-of-the-art methods. While we apply this kernel to proteins and ligands, it is applicable to any kind of 3D data where objects follow a common schema, such as RNA, cars, or standardized equipment.
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Acknowledgements
The authors would like to thank students Davy De Mits and Sunil Aryal for conducting preliminary experiments, Dr. Kurt De Grave and Dr. Fabrizio Costa for assistance with running NSPDK, and Jérôme Renaux for proofreading. This research was supported by ERC-StG 240186 MiGraNT and IWT-SBO Nemoa.
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Schietgat, L., Fannes, T., Ramon, J. (2015). Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_19
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DOI: https://doi.org/10.1007/978-3-319-24282-8_19
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