| Téléchargement | - Voir la version finale : Geometric deep learning for protein–protein interaction predictions (PDF, 2.0 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/ACCESS.2022.3201543 |
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| Auteur | Rechercher : St-Pierre Lemieux, Gabriel1; Rechercher : Paquet, Eric1Identifiant ORCID : https://orcid.org/0000-0001-6515-2556; Rechercher : Viktor, Herna L.Identifiant ORCID : https://orcid.org/0000-0003-1914-5077; Rechercher : Michalowski, Wojtek |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : National Research Council of Canada through the Artificial Intelligence for Design (AI4Design) Challenge Program |
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| Format | Texte, Article |
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| Sujet | protein-protein interaction; geometric deep learning; spectral convolutional neural network; graph convolutional neural network; macromolecular surface; heat kernel; wave kernel |
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| Résumé | This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database. Interactions are predicted from a graph representing the proteins’ three-dimensional macromolecular surfaces. The nodes are described with heat and wave kernel signatures. Twenty-one neural network architectures are proposed and compared; these are based on graph convolutional neural networks, spectral convolutional neural networks, and a novel spatio–spectral spatialized-gated convolutional neural network. The experimental results demonstrate the accuracy and the efficiency of the proposed architectures. |
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| Date de publication | 2022-08-25 |
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| Maison d’édition | IEEE |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 6615a79a-66fc-4640-881f-9aa94c806c3e |
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| Enregistrement créé | 2022-09-26 |
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| Enregistrement modifié | 2023-03-16 |
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