Bingding Huang

Orcid: 0000-0002-4748-2882

According to our database1, Bingding Huang authored at least 30 papers between 2005 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
Correction: NanoBERTa-ASP: predicting nanobody paratope based on a pretrained RoBERTa model.
BMC Bioinform., December, 2024

NanoBERTa-ASP: predicting nanobody paratope based on a pretrained RoBERTa model.
BMC Bioinform., December, 2024

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.
IEEE Trans. Neural Networks Learn. Syst., August, 2024

Deep learning to predict respiratory lung-tissue displacement from multi-phase pulmonary computed tomography images.
Biomed. Signal Process. Control., January, 2024

NanoLAS: a comprehensive nanobody database with data integration, consolidation and application.
Database J. Biol. Databases Curation, January, 2024

Label-efficient Multi-organ Segmentation Method with Diffusion Model.
CoRR, 2024

2023
Attention-based advantage actor-critic algorithm with prioritized experience replay for complex 2-D robotic motion planning.
J. Intell. Manuf., 2023

A Systematic Review for Transformer-based Long-term Series Forecasting.
CoRR, 2023

A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation.
CoRR, 2023

Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning.
CoRR, 2023

Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review.
CoRR, 2023

A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction.
Proceedings of the 23rd IEEE International Conference on Software Quality, 2023

Two-Stage Training for Abdominal Pan-Cancer Segmentation in Weak Label.
Proceedings of the Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT, 2023

Predicting Microsatellite Instability from Pathological Images Using Self-Supervised Learning Methods.
Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications, 2023

2022
A review of motion planning algorithms for intelligent robots.
J. Intell. Manuf., 2022

3D Cross Pseudo Supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation.
CoRR, 2022

Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.
Comput. Methods Programs Biomed., 2022

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.
Comput. Methods Programs Biomed., 2022

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.
Comput. Biol. Medicine, 2022

Effects of haze and dehazing on deep learning-based vision models.
Appl. Intell., 2022

Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy.
Proceedings of the Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, 2022

2021
An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios.
CoRR, 2021

A review of motion planning algorithms for intelligent robotics.
CoRR, 2021

Contrast-Enhanced CT Renal Tumor Segmentation.
Proceedings of the Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, 2021

A Cascaded 3D Segmentation Model for Renal Enhanced CT Images.
Proceedings of the Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, 2021

2020
SinoDuplex: An Improved Duplex Sequencing Approach to Detect Low-frequency Variants in Plasma cfDNA Samples.
Genom. Proteom. Bioinform., 2020

2019
BreakID: genomics breakpoints identification to detect gene fusion events using discordant pairs and split reads.
Bioinform., 2019

2011
MetaDBSite: a meta approach to improve protein DNA-binding sites prediction.
BMC Syst. Biol., 2011

Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction.
Bioinform., 2011

2005
Using residue propensities and tightness of fit to improve rigid-body protein-docking.
Proceedings of the German Conference on Bioinformatics (GCB 2005), Hamburg, 2005


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