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XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties.

, , , , , and . J. Chem. Inf. Model., 61 (6): 2697-2705 (2021)

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Stealthy control signal attacks in vector LQG systems., and . ACC, page 1179-1184. IEEE, (2016)Hyper-SAGNN: a self-attention based graph neural network for hypergraphs., , and . ICLR, OpenReview.net, (2020)False Data Injection and Detection in LQG Systems: A Game Theoretic Approach., and . IEEE Trans. Control. Netw. Syst., 7 (1): 338-348 (2020)Optimal Distribution Mapping for Inference Privacy., and . APSIPA, page 1179-1187. IEEE, (2018)pyHIVE, a health-related image visualization and engineering system using Python., , , , , , and . BMC Bioinform., 19 (1): 452:1-452:6 (2018)Correction to "XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties"., , , , , and . J. Chem. Inf. Model., 61 (9): 4820-4822 (2021)PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids., , , , , , , , and . CoRR, (2023)Optimal Local Differentially Private Quantization., and . IEEE Trans. Signal Process., (2020)AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides., , , , , , , and . CoRR, (2024)XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties., , , , , and . J. Chem. Inf. Model., 61 (6): 2697-2705 (2021)