Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D
Protein Structures

k 2021

Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

HERMOSILLA CASAJÚS, Pedro, Marco SCHÄFER, Matěj LANG, Gloria FACKELMANN, Pere-Pau VÁZQUEZ ALCOCER et. al.

Základní údaje

Originální název

Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

Autoři

HERMOSILLA CASAJÚS, Pedro (724 Španělsko), Marco SCHÄFER (276 Německo), Matěj LANG (203 Česká republika, domácí), Gloria FACKELMANN (276 Německo), Pere-Pau VÁZQUEZ ALCOCER (724 Španělsko), Barbora KOZLÍKOVÁ (203 Česká republika, domácí), Michael KRONE (276 Německo), Tobias RITSCHEL (276 Německo) a Timo ROPINSKI

Vydání

International Conference on Learning Representations (ICLR), 2021

Další údaje

Jazyk

angličtina

Typ výsledku

Prezentace na konferencích

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Scopus URL

Kód RIV

RIV/00216224:14330/21:00118838

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

classification; bioinformatics

Štítky

core_A, firank_1

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 15. 5. 2024 02:25, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

GC18-18647J, projekt VaV

Název: Vizuální analýza interakcí proteinů a ligandů (Akronym: PROLINT)

Investor: Grantová agentura ČR, Visual Analysis of Protein-Ligand Interactions

Citovat

HERMOSILLA CASAJÚS, Pedro, Marco SCHÄFER, Matěj LANG, Gloria FACKELMANN, Pere-Pau VÁZQUEZ ALCOCER, Barbora KOZLÍKOVÁ, Michael KRONE, Tobias RITSCHEL a Timo ROPINSKI. Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures. In International Conference on Learning Representations (ICLR). 2021.

@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 a 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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