Su-Ping Deng

1.2k total citations
33 papers, 820 citations indexed

About

Su-Ping Deng is a scholar working on Molecular Biology, Biomedical Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Su-Ping Deng has authored 33 papers receiving a total of 820 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 4 papers in Biomedical Engineering and 3 papers in Computational Theory and Mathematics. Recurrent topics in Su-Ping Deng's work include Bioinformatics and Genomic Networks (13 papers), Gene expression and cancer classification (8 papers) and Machine Learning in Bioinformatics (7 papers). Su-Ping Deng is often cited by papers focused on Bioinformatics and Genomic Networks (13 papers), Gene expression and cancer classification (8 papers) and Machine Learning in Bioinformatics (7 papers). Su-Ping Deng collaborates with scholars based in China, United States and India. Su-Ping Deng's co-authors include Lin Zhu, De-Shuang Huang, Zhu‐Hong You, De-Shuang Huang, Wei-Li Guo, Chun-Hou Zheng, Zhen Ji, Hongjie Yu, De-Shuang Huang and Zhen Shen and has published in prestigious journals such as Nano Letters, ACS Nano and Analytical Chemistry.

In The Last Decade

Su-Ping Deng

32 papers receiving 808 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Su-Ping Deng China 14 675 102 97 85 61 33 820
Shibiao Wan United States 21 885 1.3× 65 0.6× 53 0.5× 81 1.0× 29 0.5× 54 1.1k
Jiancheng Zhong China 13 540 0.8× 203 2.0× 96 1.0× 36 0.4× 28 0.5× 27 723
Shuaiqun Wang China 13 218 0.3× 59 0.6× 92 0.9× 100 1.2× 20 0.3× 48 470
Arturo Magana-Mora Saudi Arabia 15 588 0.9× 41 0.4× 97 1.0× 41 0.5× 91 1.5× 27 981
Ronaldo F. Hashimoto Brazil 14 501 0.7× 63 0.6× 80 0.8× 67 0.8× 33 0.5× 61 689
Quang‐Thai Ho Taiwan 14 544 0.8× 49 0.5× 46 0.5× 131 1.5× 15 0.2× 18 794
Chatchawit Aporntewan Thailand 11 241 0.4× 43 0.4× 75 0.8× 110 1.3× 48 0.8× 36 459
Burcu Bakır-Güngör Türkiye 18 365 0.5× 23 0.2× 58 0.6× 128 1.5× 75 1.2× 62 679
Tomasz Adamusiak United States 11 425 0.6× 38 0.4× 33 0.3× 140 1.6× 116 1.9× 17 594

Countries citing papers authored by Su-Ping Deng

Since Specialization
Citations

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

Fields of papers citing papers by Su-Ping Deng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Su-Ping Deng

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

All Works

20 of 20 papers shown
1.
Xu, Jing, Yujin Li, Futing Wang, et al.. (2025). Machine Learning Assisted-Intelligent Lactic Acid Monitoring in Sweat Supported by a Perspiration-Driven Self-Powered Sensor. Nano Letters. 25(7). 2968–2977. 12 indexed citations
2.
Li, Jingxian, Choong Eui Song, Siyuan Yu, et al.. (2025). Orbital DNA walker-driven ECL biosensing based on Ag3PO4@PTCA for ultrasensitive detection of Pb2+. Biosensors and Bioelectronics. 293. 118121–118121. 1 indexed citations
3.
Song, Choong Eui, Jingxian Li, Futing Wang, et al.. (2025). Multifunctional AuAgPt Nanoframes for a Stimuli-Responsive Electrochemiluminescence Aptasensor. ACS Nano. 19(28). 26161–26169. 4 indexed citations
4.
Wang, Futing, Chunxiao Zhang, Su-Ping Deng, et al.. (2024). Dual-responsive 3D DNA nanomachines cascaded hybridization chain reactions for novel self-powered flexible microRNA-detecting platform. Biosensors and Bioelectronics. 252. 116149–116149. 17 indexed citations
5.
Shen, Zhen, Su-Ping Deng, & De-Shuang Huang. (2019). Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(5). 1483–1492. 20 indexed citations
6.
Shen, Zhen, Su-Ping Deng, & De-Shuang Huang. (2019). RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(5). 1741–1750. 30 indexed citations
7.
Deng, Su-Ping & Wei-Li Guo. (2018). Identifying Key Genes of Liver Cancer by Networking of Multiple Data Sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(3). 792–800. 8 indexed citations
8.
Deng, Su-Ping, Wenxing Hu, Vince D. Calhoun, & Yu‐Ping Wang. (2017). Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(5). 1480–1491. 13 indexed citations
9.
Deng, Su-Ping, et al.. (2016). Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data. PubMed. 11. 1447–1450. 6 indexed citations
10.
Deng, Su-Ping, et al.. (2016). Diagnosing schizophrenia by integrating genomic and imaging data through network fusion. 1307–1313. 1 indexed citations
11.
Guo, Wei-Li, Lin Zhu, Su-Ping Deng, Xing‐Ming Zhao, & De-Shuang Huang. (2016). Understanding tissue-specificity with human tissue-specific regulatory networks. Science China Information Sciences. 59(7). 13 indexed citations
12.
Zhu, Lin, Su-Ping Deng, Zhu‐Hong You, & De-Shuang Huang. (2015). Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14(2). 345–352. 31 indexed citations
13.
Deng, Su-Ping, Lin Zhu, & De-Shuang Huang. (2015). Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics. 16(S3). S4–S4. 81 indexed citations
14.
Huang, De-Shuang, et al.. (2014). Prediction of Protein-Protein Interactions Based on Protein-Protein Correlation Using Least Squares Regression. Current Protein and Peptide Science. 15(6). 553–560. 82 indexed citations
15.
Deng, Su-Ping & De-Shuang Huang. (2014). SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method. Methods. 69(3). 207–212. 63 indexed citations
16.
You, Zhu‐Hong, Lin Zhu, Chun-Hou Zheng, et al.. (2014). Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinformatics. 15(S15). S9–S9. 107 indexed citations
17.
Ji, Zhiwei, Bing Wang, Su-Ping Deng, & Zhu‐Hong You. (2014). Predicting dynamic deformation of retaining structure by LSSVR-based time series method. Neurocomputing. 137. 165–172. 39 indexed citations
18.
Deng, Su-Ping, et al.. (2013). A New Approach for Identifying Protein-Coding Regions by Combining Chirp z and Wavelet Transform.. Current Bioinformatics. 8(5). 557–563. 2 indexed citations
19.
20.
Deng, Su-Ping, Yi‐Xiang Shi, Liyun Yuan, Yixue Li, & Guohui Ding. (2012). Detecting the borders between coding and non-coding DNA regions in prokaryotes based on recursive segmentation and nucleotide doublets statistics. BMC Genomics. 13(S8). S19–S19. 8 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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