Haicang Zhang
Orcid: 0000-0001-6268-4258
According to our database1,
Haicang Zhang
authored at least 15 papers
between 2014 and 2024.
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Bibliography
2024
Nat. Mac. Intell., 2024
Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
2023
Accurate and efficient protein sequence design through learning concise local environment of residues.
Bioinform., March, 2023
Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.
Genom. Proteom. Bioinform., 2023
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Predicting functional effect of missense variants using graph attention neural networks.
Nat. Mac. Intell., November, 2022
2021
BMC Bioinform., 2021
2019
Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.
BMC Bioinform., 2019
Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.
BMC Bioinform., 2019
Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling.
BMC Bioinform., 2019
2017
Improving prediction of burial state of residues by exploiting correlation among residues.
BMC Bioinform., 2017
Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.
Bioinform., 2017
2016
FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition.
Bioinform., 2016
2014
On the inapproximability of minimizing cascading failures under the deterministic threshold model.
Inf. Process. Lett., 2014