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@proceedings{1731463,
author = {Hermosilla Casajús, Pedro and Schäfer, Marco and Lang, Matěj and Fackelmann, Gloria and Vázquez Alcocer, PereandPau and Kozlíková, Barbora and Krone, Michael and Ritschel, Tobias and Ropinski, Timo},
booktitle = {International Conference on Learning Representations (ICLR)},
keywords = {classification; bioinformatics},
language = {eng},
title = {Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures},
url = {https://www.scopus.com/record/display.uri?origin=inward&eid=2-s2.0-85136230434},
year = {2021}
}
TY - CONF
ID - 1731463
AU - Hermosilla Casajús, Pedro - Schäfer, Marco - Lang, Matěj - Fackelmann, Gloria - Vázquez Alcocer, Pere-Pau - Kozlíková, Barbora - Krone, Michael - Ritschel, Tobias - Ropinski, Timo
PY - 2021
TI - Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
KW - classification
KW - bioinformatics
UR - https://www.scopus.com/record/display.uri?origin=inward&eid=2-s2.0-85136230434
N2 - The result is a paper (16 pages) at International Conference on Learning Representations. Although it is among the very best conferences in CS, since its proceedings do not have an ISBN or ISSN, the result cannot be transferred to the RIV database as a result of type D. The original abstract follows: Proteins perform a large variety of functions in living organisms and thus play a key role in biology. However, commonly used algorithms in protein learning were not specifically designed for protein data, and are therefore not able to capture all relevant structural levels of a protein during learning. To fill this gap, we propose two new learning operators, specifically designed to process protein structures. First, we introduce a novel convolution operator that considers the primary, secondary, and tertiary structure of a protein by using n-D convolutions defined on both the Euclidean distance, as well as multiple geodesic distances between the atoms in a multi-graph. Second, we introduce a set of hierarchical pooling operators that enable multi-scale protein analysis. We further evaluate the accuracy of our algorithms on common downstream tasks, where we outperform state-of-the-art protein learning algorithms.
ER -
HERMOSILLA CASAJÚS, Pedro, Marco SCHÄFER, Matěj LANG, Gloria FACKELMANN, Pere-Pau VÁZQUEZ ALCOCER, Barbora KOZLÍKOVÁ, Michael KRONE, Tobias RITSCHEL and Timo ROPINSKI. Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures. In \textit{International Conference on Learning Representations (ICLR)}. 2021.
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