Bingding Huang

3.3k total citations · 1 hit paper
59 papers, 2.1k citations indexed

About

Bingding Huang is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Bingding Huang has authored 59 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 14 papers in Computer Vision and Pattern Recognition and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Bingding Huang's work include Protein Structure and Dynamics (7 papers), Advanced Neural Network Applications (7 papers) and Cancer Genomics and Diagnostics (6 papers). Bingding Huang is often cited by papers focused on Protein Structure and Dynamics (7 papers), Advanced Neural Network Applications (7 papers) and Cancer Genomics and Diagnostics (6 papers). Bingding Huang collaborates with scholars based in China, Germany and Finland. Bingding Huang's co-authors include Michael Schroeder, Biaoyang Lin, Zengming Zhang, Pasi Fränti, Yu Li, Haseeb Hassan, Outi M. H. Salo‐Ahen, Gabriele Cruciani, Stefan Henrich and Rebecca C. Wade and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Bioinformatics.

In The Last Decade

Bingding Huang

53 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A systematic review for transformer-based long-term series forecasting

2025 34 citations Bingding Huang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Bingding Huang China	18	1.1k	543	268	192	187	59	2.1k
Juan Liu China	26	1.2k 1.0×	305 0.6×	255 1.0×	138 0.7×	123 0.7×	168	2.2k
Rengül Çetin-Atalay Türkiye	33	2.0k 1.8×	670 1.2×	98 0.4×	165 0.9×	312 1.7×	140	3.8k
Khalid Raza India	23	558 0.5×	352 0.6×	103 0.4×	83 0.4×	100 0.5×	107	1.5k
Ningbo Zhang China	23	880 0.8×	224 0.4×	192 0.7×	76 0.4×	89 0.5×	141	2.4k
Xiaochen Bo China	31	2.7k 2.3×	567 1.0×	171 0.6×	73 0.4×	141 0.8×	166	4.2k
Fengfeng Zhou China	31	1.9k 1.7×	168 0.3×	319 1.2×	199 1.0×	107 0.6×	170	3.4k
Yi Xiong China	32	2.2k 2.0×	807 1.5×	193 0.7×	73 0.4×	249 1.3×	126	3.1k
Stefan Krämer Germany	22	1.0k 0.9×	413 0.8×	127 0.5×	106 0.6×	83 0.4×	70	2.3k
Nguyen Quoc Khanh Le Taiwan	43	2.0k 1.7×	411 0.8×	656 2.4×	207 1.1×	97 0.5×	151	3.9k
Ali Masoudi‐Nejad Iran	34	2.4k 2.1×	961 1.8×	68 0.3×	104 0.5×	222 1.2×	165	3.7k

Countries citing papers authored by Bingding Huang

Since Specialization

Citations

This map shows the geographic impact of Bingding Huang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bingding Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bingding Huang more than expected).

Fields of papers citing papers by Bingding Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bingding Huang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bingding Huang. The network helps show where Bingding Huang may publish in the future.

Co-authorship network of co-authors of Bingding Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Bingding Huang. A scholar is included among the top collaborators of Bingding Huang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bingding Huang. Bingding Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Li, Pengfei, Zheng Wang, Zhuang Tong, et al.. (2025). Preliminary clinical experience with a robot-assisted system in preoperative hookwire localization of pulmonary nodules: a prospective pilot study. Journal of Robotic Surgery. 19(1). 690–690.

Zhang, Qifeng, et al.. (2025). A Review of Touching-Based Underwater Robotic Perception and Manipulation. Machines. 13(1). 41–41. 2 indexed citations

Chen, Chao, et al.. (2025). RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans. Complex & Intelligent Systems. 11(2). 4 indexed citations

Liu, Zhengwen, et al.. (2024). NanoLAS: a comprehensive nanobody database with data integration, consolidation and application. Database. 2024. 7 indexed citations

Huang, Bingding, et al.. (2024). Visual Insight: Deep Multilayer Fusion with Inception-Based LSTM for Descriptive Image Captioning. 124–131.

Huang, Bingding, et al.. (2024). Automated Tomato Leaf Disease Detection Using Image Processing: An SVM‐Based Approach with GLCM and SIFT Features. Journal of Engineering. 2024(1). 8 indexed citations

Hassan, Haseeb, et al.. (2024). Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification. Heliyon. 10(10). e30528–e30528. 4 indexed citations

Chen, Chao, Wen Zhong, Yang Liu, et al.. (2024). MLAU-Net: Deep supervised attention and hybrid loss strategies for enhanced segmentation of low-resolution kidney ultrasound. Digital Health. 10. 599935018–599935018. 1 indexed citations

Meng, Xiangpeng, et al.. (2024). NanoBERTa-ASP: predicting nanobody paratope based on a pretrained RoBERTa model. BMC Bioinformatics. 25(1). 122–122. 6 indexed citations

10.

Chen, Zhuo, Yang Liu, Haseeb Hassan, et al.. (2024). Comprehensive 3D Analysis of the Renal System and Stones: Segmenting and Registering Non-Contrast and Contrast Computed Tomography Images. Information Systems Frontiers. 27(1). 97–111. 5 indexed citations

11.

Hassan, Haseeb, et al.. (2024). Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging. Frontiers in Neuroscience. 18. 1363930–1363930. 12 indexed citations

12.

Liu, Yang, Jinyu Tian, Zhuo Chen, et al.. (2024). Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset. Interdisciplinary Sciences Computational Life Sciences. 16(2). 439–454. 1 indexed citations

13.

Liang, Shibo, et al.. (2023). A benchmark study of protein folding algorithms on nanobodies. 1 indexed citations

14.

Li, Jing, Jun Liu, Wei Cui, et al.. (2023). Optical navigation robot-assisted puncture system for accurate lung nodule biopsy: an animal study. Quantitative Imaging in Medicine and Surgery. 13(12). 7789–7801. 6 indexed citations

15.

Hassan, Haseeb, et al.. (2022). Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. Computer Methods and Programs in Biomedicine. 218. 106731–106731. 38 indexed citations

16.

Cui, Pin, Wenfeng Ma, Zhiyi Han, et al.. (2022). Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles. Genomics. 114(6). 110502–110502. 7 indexed citations

17.

Hassan, Haseeb, Dan Li, Shaohua Xiang, et al.. (2021). Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Computers in Biology and Medicine. 141. 105123–105123. 43 indexed citations

18.

Zhang, Yang, Fengying Liu, Shaohua Xiang, et al.. (2020). SinoDuplex: An Improved Duplex Sequencing Approach to Detect Low-Frequency Variants in Plasma cfDNA Samples. Genomics Proteomics & Bioinformatics. 18(1). 81–90. 9 indexed citations

19.

Li, Lisha, Jie Liu, Wei Yu, et al.. (2013). Deep Transcriptome Profiling of Ovarian Cancer Cells Using Next-Generation Sequencing Approach. Methods in molecular biology. 1049. 139–169. 3 indexed citations

20.

Kokh, Daria B., Bingding Huang, Rebecca C. Wade, & Peter J. Winn. (2009). Modeling of Protein Adsorption on a Metal Surface: Brownian Dynamics Simulations. Biophysical Journal. 96(3). 298a–299a. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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