Wanshan Ning

5.2k total citations
29 papers, 858 citations indexed

About

Wanshan Ning is a scholar working on Molecular Biology, Infectious Diseases and Oncology. According to data from OpenAlex, Wanshan Ning has authored 29 papers receiving a total of 858 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 5 papers in Infectious Diseases and 4 papers in Oncology. Recurrent topics in Wanshan Ning's work include Machine Learning in Bioinformatics (5 papers), COVID-19 Clinical Research Studies (5 papers) and Ubiquitin and proteasome pathways (5 papers). Wanshan Ning is often cited by papers focused on Machine Learning in Bioinformatics (5 papers), COVID-19 Clinical Research Studies (5 papers) and Ubiquitin and proteasome pathways (5 papers). Wanshan Ning collaborates with scholars based in China, United States and Japan. Wanshan Ning's co-authors include Yu Xue, Yaping Guo, Peiran Jiang, Di Peng, Xiaodan Tan, Chenwei Wang, Weizhi Zhang, Shaofeng Lin, Han Cheng and Yujie Gou and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Circulation Research.

In The Last Decade

Wanshan Ning

26 papers receiving 854 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wanshan Ning China	15	539	110	77	72	69	29	858
Yaping Guo China	13	501 0.9×	108 1.0×	106 1.4×	54 0.8×	61 0.9×	26	756
Rani K. Powers United States	8	599 1.1×	52 0.5×	111 1.4×	71 1.0×	80 1.2×	12	1.1k
Betsy Gregory United States	19	557 1.0×	87 0.8×	154 2.0×	36 0.5×	43 0.6×	30	1.4k
Naiem T. Issa United States	11	323 0.6×	50 0.5×	55 0.7×	41 0.6×	31 0.4×	49	749
Peiran Jiang China	9	369 0.7×	114 1.0×	50 0.6×	29 0.4×	76 1.1×	12	583
Jianbo Fu China	17	806 1.5×	53 0.5×	185 2.4×	31 0.4×	90 1.3×	28	1.2k
Saikat Chakrabarti India	19	609 1.1×	47 0.4×	186 2.4×	95 1.3×	20 0.3×	57	1.1k
Shiva Kumar India	8	670 1.2×	40 0.4×	130 1.7×	57 0.8×	41 0.6×	13	1.1k
Matteo Floris Italy	19	763 1.4×	52 0.5×	185 2.4×	41 0.6×	20 0.3×	57	1.3k

Countries citing papers authored by Wanshan Ning

Since Specialization

Citations

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

Fields of papers citing papers by Wanshan Ning

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wanshan Ning. 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 Wanshan Ning. The network helps show where Wanshan Ning may publish in the future.

Co-authorship network of co-authors of Wanshan Ning

This figure shows the co-authorship network connecting the top 25 collaborators of Wanshan Ning. A scholar is included among the top collaborators of Wanshan Ning 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 Wanshan Ning. Wanshan Ning 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

Yang, Ni, Xiaolu Shi, Ri Wen, et al.. (2025). Succinylation of SERCA2a at K352 Promotes Its Ubiquitinoylation and Degradation by Proteasomes in Sepsis-Induced Heart Dysfunction. Circulation Heart Failure. 18(4). e012180–e012180. 3 indexed citations

Ning, Wanshan, et al.. (2025). HybridKla: a hybrid deep learning framework for lactylation site prediction. Briefings in Bioinformatics. 26(4). 1 indexed citations

Zhang, Tie‐Ning, Xinmei Huang, Xiaolu Shi, et al.. (2025). Lactylation of HADHA Promotes Sepsis-Induced Myocardial Depression. Circulation Research. 137(4). e65–e87. 2 indexed citations

Liu, Tommy, Wanshan Ning, Tong Xu, et al.. (2025). Wheat Yield Prediction Based on Parallel CNN-LSTM-Attention with Transfer Learning Model. Agriculture. 15(23). 2519–2519.

Gu, Ying, Zhicheng Wang, Shuai Liu, et al.. (2025). Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests. Scientific Reports. 15(1). 13044–13044.

Ning, Wanshan, et al.. (2024). Blockchain-Based Federated Learning: A Survey and New Perspectives. Applied Sciences. 14(20). 9459–9459. 13 indexed citations

Zou, Danyi, et al.. (2024). CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer. Computational and Structural Biotechnology Journal. 23. 2507–2515.

Chen, Qihong, Ruizhi Xu, Ying Gu, et al.. (2024). Respiratory pathogen analysis in pediatric inpatients unraveled the infection pattern of Mycoplasma pneumoniae post the COVID-19 pandemic. Frontiers in Public Health. 12. 1437508–1437508. 3 indexed citations

Gu, Ying, et al.. (2024). Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data. Cardiovascular Diabetology. 23(1). 351–351. 20 indexed citations

10.

Wang, Chenwei, Shanshan Ma, Qinyu Li, et al.. (2023). Small-sample learning reveals propionylation in determining global protein homeostasis. Nature Communications. 14(1). 2813–2813. 9 indexed citations

11.

Li, Cheng, et al.. (2022). Construction and Validation of Mortality Risk Nomograph Model for Severe/Critical Patients with COVID-19. Diagnostics. 12(10). 2562–2562. 6 indexed citations

12.

Jiang, Peiran, et al.. (2021). FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Computational and Structural Biotechnology Journal. 19. 4497–4509. 40 indexed citations

13.

Zhu, Han, Weizhi Zhang, Wanshan Ning, et al.. (2021). Model-based analysis uncovers mutations altering autophagy selectivity in human cancer. Nature Communications. 12(1). 3258–3258. 22 indexed citations

14.

Wang, Chong, Xufang Li, Wanshan Ning, et al.. (2021). Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19. Theranostics. 11(16). 8008–8026. 26 indexed citations

15.

Wang, Chenwei, Xiaodan Tan, Dachao Tang, et al.. (2021). GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites. Briefings in Bioinformatics. 23(2). 40 indexed citations

16.

Ning, Wanshan, Haodong Xu, Peiran Jiang, et al.. (2020). HybridSucc: A Hybrid-Learning Architecture for General and Species-Specific Succinylation Site Prediction. Genomics Proteomics & Bioinformatics. 18(2). 194–207. 34 indexed citations

17.

Ning, Wanshan, Shijun Lei, Jingjing Yang, et al.. (2020). Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning. Nature Biomedical Engineering. 4(12). 1197–1207. 131 indexed citations

18.

Guo, Yaping, Wanshan Ning, Peiran Jiang, et al.. (2020). GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains. Cells. 9(5). 1266–1266. 14 indexed citations

19.

Ning, Wanshan, Peiran Jiang, Yaping Guo, et al.. (2020). GPS-Palm: a deep learning-based graphic presentation system for the prediction ofS-palmitoylation sites in proteins. Briefings in Bioinformatics. 22(2). 1836–1847. 85 indexed citations

20.

Liu, Qiang, Wanshan Ning, Robert Dantzer, Gregory G. Freund, & Keith W. Kelley. (1998). Activation of protein kinase C-zeta and phosphatidylinositol 3'-kinase and promotion of macrophage differentiation by insulin-like growth factor-I.. PubMed. 160(3). 1393–401. 78 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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