Advanced atom-level representations for protein flexibility prediction utilizing graph neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Sarparast, Sina, Zaimi, Aldo, Ebert, Maximilian, Goldsmith, Michael-Rock |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
by: Ebeid, Islam Akef, et al.
Published: (2025)
by: Ebeid, Islam Akef, et al.
Published: (2025)
Do graph neural network states contain graph properties?
by: Pelletreau-Duris, Tom, et al.
Published: (2024)
by: Pelletreau-Duris, Tom, et al.
Published: (2024)
Enhancing material property prediction with ensemble deep graph convolutional networks
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2024)
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2024)
Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
by: Volzhin, Nikita, et al.
Published: (2025)
by: Volzhin, Nikita, et al.
Published: (2025)
Effective backdoor attack on graph neural networks in link prediction tasks
by: Dai, Jiazhu, et al.
Published: (2024)
by: Dai, Jiazhu, et al.
Published: (2024)
Do deep neural networks utilize the weight space efficiently?
by: Koyun, Onur Can, et al.
Published: (2024)
by: Koyun, Onur Can, et al.
Published: (2024)
Addressing divergent representations from causal interventions on neural networks
by: Grant, Satchel, et al.
Published: (2025)
by: Grant, Satchel, et al.
Published: (2025)
Applying graph neural network to SupplyGraph for supply chain network
by: Han, Kihwan
Published: (2024)
by: Han, Kihwan
Published: (2024)
Crystal structure prediction using graph neural combinatorial optimization
by: Gerolymatos, Stavros, et al.
Published: (2026)
by: Gerolymatos, Stavros, et al.
Published: (2026)
Robustness questions the interpretability of graph neural networks: what to do?
by: Lukyanov, Kirill, et al.
Published: (2025)
by: Lukyanov, Kirill, et al.
Published: (2025)
sHGCN: Simplified hyperbolic graph convolutional neural networks
by: Arévalo, Pol, et al.
Published: (2025)
by: Arévalo, Pol, et al.
Published: (2025)
Decomposing heterogeneous dynamical systems with graph neural networks
by: Allier, Cédric, et al.
Published: (2024)
by: Allier, Cédric, et al.
Published: (2024)
Why are hyperbolic neural networks effective? A study on hierarchical representation capability
by: Tan, Shicheng, et al.
Published: (2024)
by: Tan, Shicheng, et al.
Published: (2024)
Towards physics-informed neural networks for landslide prediction
by: Dahal, Ashok, et al.
Published: (2024)
by: Dahal, Ashok, et al.
Published: (2024)
Development of a graph neural network surrogate for travel demand modelling
by: Makarov, Nikita, et al.
Published: (2024)
by: Makarov, Nikita, et al.
Published: (2024)
Task complexity shapes internal representations and robustness in neural networks
by: Jankowski, Robert, et al.
Published: (2025)
by: Jankowski, Robert, et al.
Published: (2025)
Distill n' Explain: explaining graph neural networks using simple surrogates
by: Pereira, Tamara, et al.
Published: (2023)
by: Pereira, Tamara, et al.
Published: (2023)
Planning in a recurrent neural network that plays Sokoban
by: Taufeeque, Mohammad, et al.
Published: (2024)
by: Taufeeque, Mohammad, et al.
Published: (2024)
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
by: Vashishtha, Govind, et al.
Published: (2025)
by: Vashishtha, Govind, et al.
Published: (2025)
Integrating Bayesian methods with neural network--based model predictive control: a review
by: Karacelik, Asli
Published: (2025)
by: Karacelik, Asli
Published: (2025)
Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
by: Bian, Zilin, et al.
Published: (2024)
by: Bian, Zilin, et al.
Published: (2024)
Human alignment of neural network representations
by: Muttenthaler, Lukas, et al.
Published: (2022)
by: Muttenthaler, Lukas, et al.
Published: (2022)
Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
by: Salloom, Tony, et al.
Published: (2025)
by: Salloom, Tony, et al.
Published: (2025)
Unifying approach to uniform expressivity of graph neural networks
by: Luo, Huan, et al.
Published: (2026)
by: Luo, Huan, et al.
Published: (2026)
Flow reconstruction in time-varying geometries using graph neural networks
by: Danciu, Bogdan A., et al.
Published: (2024)
by: Danciu, Bogdan A., et al.
Published: (2024)
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
by: Wu, Hongfei, et al.
Published: (2024)
by: Wu, Hongfei, et al.
Published: (2024)
On permutation-invariant neural networks
by: Kimura, Masanari, et al.
Published: (2024)
by: Kimura, Masanari, et al.
Published: (2024)
Sobolev acceleration for neural networks
by: Oh, Jong Kwon, et al.
Published: (2025)
by: Oh, Jong Kwon, et al.
Published: (2025)
Attention mechanisms in neural networks
by: Hays, Hasi
Published: (2026)
by: Hays, Hasi
Published: (2026)
Principles of Lipschitz continuity in neural networks
by: Luo, Róisín
Published: (2026)
by: Luo, Róisín
Published: (2026)
Linearity-based neural network compression
by: Dobler, Silas, et al.
Published: (2025)
by: Dobler, Silas, et al.
Published: (2025)
Community detection robustness of graph neural networks
by: Goel, Jaidev, et al.
Published: (2025)
by: Goel, Jaidev, et al.
Published: (2025)
On convex decision regions in deep network representations
by: Tětková, Lenka, et al.
Published: (2023)
by: Tětková, Lenka, et al.
Published: (2023)
A simple connection from loss flatness to compressed neural representations
by: Chen, Shirui, et al.
Published: (2023)
by: Chen, Shirui, et al.
Published: (2023)
Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction
by: Kargar, Masoud, et al.
Published: (2024)
by: Kargar, Masoud, et al.
Published: (2024)
HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks
by: Li, Jiannuo, et al.
Published: (2025)
by: Li, Jiannuo, et al.
Published: (2025)
Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning
by: Dai, Wenfeng, et al.
Published: (2025)
by: Dai, Wenfeng, et al.
Published: (2025)
Towards graph neural networks for provably solving convex optimization problems
by: Qian, Chendi, et al.
Published: (2025)
by: Qian, Chendi, et al.
Published: (2025)
Understanding the dynamics of the frequency bias in neural networks
by: Molina, Juan, et al.
Published: (2024)
by: Molina, Juan, et al.
Published: (2024)
Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)
by: Tierz, Alicia, et al.
Published: (2024)
Similar Items
-
Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
by: Ebeid, Islam Akef, et al.
Published: (2025) -
Do graph neural network states contain graph properties?
by: Pelletreau-Duris, Tom, et al.
Published: (2024) -
Enhancing material property prediction with ensemble deep graph convolutional networks
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2024) -
Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
by: Volzhin, Nikita, et al.
Published: (2025) -
Effective backdoor attack on graph neural networks in link prediction tasks
by: Dai, Jiazhu, et al.
Published: (2024)