De-Shuang Huang

16.7k citations

331 papers · 11.1k indexed · 2 hit papers · h-index 58

Computer Vision and Pattern Recognition top 0.2%
- Face and Expression Recognition 31
Signal Processing top 0.5%
Artificial Intelligence top 0.5%
- Neural Networks and Applications 49
Media Technology top 0.5%
Cancer Research top 2%
Molecular Biology
- Machine Learning in Bioinformatics 44
- Bioinformatics and Genomic Networks 36
- RNA and protein synthesis mechanisms 29
- Gene expression and cancer classification 25
- Protein Structure and Dynamics 23
- Genomics and Phylogenetic Studies 23

Co-authors: Ji‐Xiang Du Zhu‐Hong You Chun-Hou Zheng Wei Jia Xiaofeng Wang Lin Zhu David Zhang Huan Xu
Cited by: Computer Vision and Pattern Recognition Signal Processing Artificial Intelligence
Partner nations: China South Korea United Kingdom

In The Last Decade

De-Shuang Huang

317 papers receiving 10.8k citations

Hit Papers

align trajectories log scale

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.

2018 DeepCRISPR: optimized CRISPR guide RNA design by deep learning
2009 An efficient local Chan–Vese model for image segmentation

Peers

Replace Chen Ding with:

Chen Ding China

Fuhui Long United States

Ujjwal Maulik India

Heng Huang United States

Chenggang Yan China

Alexander Zien Germany

Sanghamitra Bandyopadhyay India

Sungroh Yoon South Korea

Mengjie Zhang New Zealand

Asoke K. Nandi United Kingdom

De-Shuang Huang relative to Chen Ding China Chen Ding's profile →

Citations per field

00.5×2×4×6.4×

Chen Ding · 1×

×1.5 3k/2k

CVPR

×1.6 1k/806

SP

×0.8 3k/3k

AI

×1.0 547/535

MT

×6.4 926/144

CR

Citations per year

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

Countries citing papers authored by De-Shuang Huang

Since Specialization

Citations

This map shows the geographic impact of De-Shuang 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 De-Shuang Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites De-Shuang Huang more than expected).

Fields of papers citing papers by De-Shuang Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by De-Shuang 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 De-Shuang Huang. The network helps show where De-Shuang Huang may publish in the future.

Co-authorship network

The 25 scholars most cited alongside De-Shuang Huang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with De-Shuang Huang Line = papers co-authored together De-Shuang Huang links everyone, so they are left out of the graph.

All Works

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

20 of 20 papers shown

#	Work
1	Spatio-temporal graph-based self-labeling for video anomaly detection Meng Xing,Zhiyong Feng,Yong Su,Yiming Zhang,Changjae Oh,Valeriya Gribova,Vladimir Fedorovich Filaretoy,De-Shuang Huang	2025	1
2	Beyond singular prototype: A prototype splitting strategy for few-shot medical image segmentation Pengrui Teng,Wenjian Liu,Xuesong Wang,Di Wu,Changan Yuan,Yuhu Cheng,De-Shuang Huang	2024	2
3	GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site Chuah Chai Wen,Wanxian He,De-Shuang Huang	2024	1
4	Applied Intelligence De-Shuang Huang,Prashan Premaratne,Changan Yuan	2024	1
5	Hierarchical Multiview Top-k Pooling With Deep-Q-Networks Zhipeng Li,Hailong Su,Yong Wu,Qinhu Zhang,Changan Yuan,Valeriya Gribova,Vladimir Filaretov,De-Shuang Huang	2023	2
6	Computational prediction and characterization of cell-type-specific and shared binding sites Qinhu Zhang,Pengrui Teng,Siguo Wang,Ying He,Zhen Cui,Zhenghao Guo,Yixin Liu,Changan Yuan,Qi Liu,De-Shuang Huang	2022	9
7	Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture Siguo Wang,Qinhu Zhang,Zhen Shen,Ying He,Zhan‐Heng Chen,Jianqiang Li,De-Shuang Huang	2021	39
8	RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification Zhen Cui,Zhan‐Heng Chen,Qinhu Zhang,Valeriya Gribova,Vladimir Filaretov,De-Shuang Huang	2021	6
9	MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm Zhen-Hao Guo,Zhu‐Hong You,De-Shuang Huang,Hai-Cheng Yi,Kai Zheng,Zhan‐Heng Chen,Yanbin Wang	2020	32
10	A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information Hai-Cheng Yi,Zhu‐Hong You,De-Shuang Huang,Li Xiao,Tonghai Jiang,Liping Li	2018	96
11	High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites Qinhu Zhang,Lin Zhu,De-Shuang Huang	2018	80
12	Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding Qinhu Zhang,Lin Zhu,Wenzheng Bao,De-Shuang Huang	2018	62
13	A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction Xing Chen,Zhichao Jiang,Di Xie,De-Shuang Huang,Qi Zhao,Guiying Yan,Zhu‐Hong You	2017	44
14	An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency Wei-Li Guo,De-Shuang Huang	2017	16
15	Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information Jiyong An,Zhu‐Hong You,Xing Chen,De-Shuang Huang,Guiying Yan,Da-Fu Wang	2016	9
16	Intelligent Computing Theories and Application : 12th International Conference, ICIC 2016, Lanzhou, China, August 2-5, 2016, Proceedings, Part II Vitoantonio Bevilacqua,Prashan Premaratne,De-Shuang Huang	2016	1
17	Collaborative Completion of Transcription Factor Binding Profiles via Local Sensitive Unified Embedding Lin Zhu,Wei-Li Guo,Canyi Lu,De-Shuang Huang	2016	3
18	Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Proceedings De-Shuang Huang,Kang-Hyun Jo,Ling Wang	2014	5
19	Intelligent Computing in Signal Processing and Pattern Recognition De-Shuang Huang,Kang Li,G.W. Irwin	2006	18
20	Intelligent computing in signal processing and pattern recognition : International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August, 16-19 2006 De-Shuang Huang,Kang Li,G.W. Irwin	2006	1

About De-Shuang Huang

De-Shuang Huang is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Signal Processing, having authored 331 papers that have together received 11.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (49 papers), Machine Learning in Bioinformatics (44 papers), Bioinformatics and Genomic Networks (36 papers), Face and Expression Recognition (31 papers), RNA and protein synthesis mechanisms (29 papers), Gene expression and cancer classification (25 papers), Protein Structure and Dynamics (23 papers) and Genomics and Phylogenetic Studies (23 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.4k citations), Signal Processing (1.3k citations) and Artificial Intelligence (2.6k citations). De-Shuang Huang has collaborated with scholars based in China, South Korea and United Kingdom. Frequent co-authors include Ji‐Xiang Du, Zhu‐Hong You, Chun-Hou Zheng, Wei Jia, Xiaofeng Wang, Lin Zhu, David Zhang, Huan Xu, Zhong‐Qiu Zhao and Fei Han.

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.

Explore authors with similar magnitude of impact