Publication details
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
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| Year of publication | 2021 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| Description | 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. |
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