Nai-Yang Deng

5.0k citations

98 papers · 4.0k indexed · 1 hit paper · h-index 34

Computer Vision and Pattern Recognition top 0.5%
Artificial Intelligence top 0.5%
Molecular Biology top 10%
Control and Systems Engineering top 1%
Computational Theory and Mathematics top 1%

Co-authors: Yuan‐Hai Shao Wei-Jie ChenZhen WangYingjie Tian Chun‐Na Li Ling‐Yun WuYan XuChunhua Zhang
Topics: Face and Expression Recognition (58 papers)Machine Learning in Bioinformatics (23 papers)Machine Learning and ELM (21 papers)
Cited by: Computer Vision and Pattern Recognition Artificial Intelligence Control and Systems Engineering
Journals: Bioinformatics PLoS ONE Scientific Reports
Partner nations: China Australia Japan

In The Last Decade

Nai-Yang Deng

95 papers receiving 3.9k 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.

Improvements on Twin Support Vector Machines

2011 460 citations Yuan‐Hai Shao, Chunhua Zhang et al. IEEE Transactions on Neural Networks profile →

Peers

Replace Yuan‐Hai Shao with:

Yuan‐Hai Shao China

Suresh Chandra India

Catherine Blake United States

Huaxiang Zhang China

Jie Gui China

Yitian Xu China

Vikas Sindhwani United States

Jian Yu China

Ping Li China

Dezhong Peng China

Nai-Yang Deng relative to Yuan‐Hai Shao China Yuan‐Hai Shao's profile →

Citations per field

00.5×2×4×5.8×

Yuan‐Hai Shao · 1×

×0.8 2k/2k

CVPR

×0.8 2k/2k

AI

×5.8 1k/212

MB

×0.9 720/825

CSE

×4.7 437/93

CTM

Citations per year

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Countries citing papers authored by Nai-Yang Deng

Since Specialization

Citations

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

Fields of papers citing papers by Nai-Yang Deng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nai-Yang Deng. 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 Nai-Yang Deng. The network helps show where Nai-Yang Deng may publish in the future.

Co-authorship network of co-authors of Nai-Yang Deng

This figure shows the co-authorship network connecting the top 25 collaborators of Nai-Yang Deng. A scholar is included among the top collaborators of Nai-Yang Deng 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 Nai-Yang Deng. Nai-Yang Deng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Union nonparallel support vector machines framework with consistency 2023 ·Applied Soft Computing ·Chun‐Na Li,Yuan‐Hai Shao,(unknown),(unknown),(unknown),Naihua Xiu,Nai-Yang Deng	3
2	Robust 2DPCA by Tℓ₁ Criterion Maximization for Image Recognition 2021 ·IEEE Access ·(unknown),(unknown),Liming Liu,Yuan‐Hai Shao,(unknown),Nai-Yang Deng	2
3	Prediction of sumoylation sites in proteins using linear discriminant analysis 2015 ·Gene ·Yan Xu,(unknown),Nai-Yang Deng,Liming Liu	21
4	Phogly–PseAAC: Prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity 2015 ·Journal of Theoretical Biology ·Yan Xu,(unknown),Jun Ding,Ling‐Yun Wu,Nai-Yang Deng	20
5	iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition 2014 ·PLoS ONE ·Yan Xu,Xin Wen,(unknown),Ling‐Yun Wu,Nai-Yang Deng,Kuo‐Chen Chou	217
6	Correction: Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data 2013 ·PLoS ONE ·Yongcui Wang,Shilong Chen,Nai-Yang Deng,(unknown)	4
7	Computational Study of Drugs by Integrating Omics Data with Kernel Methods 2013 ·Molecular Informatics ·(unknown),(unknown),Nai-Yang Deng,Shilong Chen,(unknown),(unknown)	12
8	Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data 2013 ·PLoS ONE ·Yongcui Wang,Shilong Chen,Nai-Yang Deng,Yong Wang	117
9	Prediction of posttranslational modification sites from amino acid sequences with kernel methods 2013 ·Journal of Theoretical Biology ·Yan Xu,Xiaobo Wang,Yongcui Wang,Yingjie Tian,Xiaojian Shao,Ling‐Yun Wu,Nai-Yang Deng	40
10	Constructing support vector machine ensemble with segmentation for imbalanced datasets 2012 ·Neural Computing and Applications ·Qian Li,Bing Yang,Yi Li,Nai-Yang Deng,Ling Jing	22
11	Improvements on Twin Support Vector Machinesbreakdown → 2011 ·IEEE Transactions on Neural Networks ·Yuan‐Hai Shao,Chunhua Zhang,Xiaobo Wang,Nai-Yang Deng	460
12	An Efficient Support Vector Machine Approach for Identifying Protein S-nitrosylation Sites 2011 ·Protein and Peptide Letters ·Yuxin Li,Yuan‐Hai Shao,(unknown),Nai-Yang Deng	26
13	Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation 2011 ·BMC Bioinformatics ·Xianwen Ren,Yongcui Wang,Yong Wang,Xiang‐Sun Zhang,Nai-Yang Deng	7
14	Improved Prediction of Palmitoylation Sites Using PWMs and SVM 2011 ·Protein and Peptide Letters ·Yuxin Li,Yuan‐Hai Shao,Nai-Yang Deng	27
15	Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context 2011 ·BMC Systems Biology ·Yongcui Wang,Yong Wang,Zhixia Yang,Nai-Yang Deng	32
16	A coordinate descent margin based-twin support vector machine for classification 2011 ·Neural Networks ·Yuan‐Hai Shao,Nai-Yang Deng	87
17	Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature 2010 ·Protein and Peptide Letters ·Yongcui Wang,Xiaobo Wang,Zhixia Yang,Nai-Yang Deng	61
18	Lysine acetylation sites prediction using an ensemble of support vector machine classifiers 2010 ·Journal of Theoretical Biology ·Yan Xu,Xiaobo Wang,Jun Ding,Ling‐Yun Wu,Nai-Yang Deng	55
19	Distinguishing enzymes from non-enzymes via support vector machine 2008 ·Open Forum Infectious Diseases ·Yongcui Wang,Yingjie Tian,Nai-Yang Deng	3
20	Accurate Prediction of Translation Initiation Sites by Universum SVM 2008 ·Tingting Gao,Yingjie Tian,(unknown),Nai-Yang Deng	9

About Nai-Yang Deng

Nai-Yang Deng is a scholar working on Computer Vision and Pattern Recognition, Computational Mathematics and Artificial Intelligence, having authored 98 papers that have together received 4.0k indexed citations. Recurring topics across this work include Face and Expression Recognition (58 papers), Machine Learning in Bioinformatics (23 papers) and Machine Learning and ELM (21 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.0k citations), Artificial Intelligence (1.8k citations) and Control and Systems Engineering (720 citations). Nai-Yang Deng has collaborated with scholars based in China, Australia and Japan. Frequent co-authors include Yuan‐Hai Shao, Wei-Jie Chen, Zhen Wang, Yingjie Tian, Chun‐Na Li, Ling‐Yun Wu, Yan Xu, Chunhua Zhang, Xiaobo Wang and Chunhua Zhang. Their work appears in journals such as Bioinformatics, PLoS ONE and Scientific Reports.

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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