Geometric Deep Learning for Protein–Protein Interaction Predictions

Geometric Deep Learning for Protein–Protein Interaction Predictions

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 prote...

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Bibliographic Details
Main Authors: Gabriel St-Pierre Lemieux, Eric Paquet, Herna L Viktor, Wojtek Michalowski
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Protein > protein interaction
geometric deep learning
spectral convolutional neural network
graph convolutional neural network
macromolecular surface
heat kernel
Online Access:https://ieeexplore.ieee.org/document/9866765/
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https://ieeexplore.ieee.org/document/9866765/

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