Nan Sheng

976 total citations
68 papers, 616 citations indexed

About

Nan Sheng is a scholar working on Molecular Biology, Cancer Research and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nan Sheng has authored 68 papers receiving a total of 616 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 26 papers in Cancer Research and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nan Sheng's work include Cancer-related molecular mechanisms research (23 papers), RNA modifications and cancer (15 papers) and MicroRNA in disease regulation (11 papers). Nan Sheng is often cited by papers focused on Cancer-related molecular mechanisms research (23 papers), RNA modifications and cancer (15 papers) and MicroRNA in disease regulation (11 papers). Nan Sheng collaborates with scholars based in China, Iran and United Kingdom. Nan Sheng's co-authors include Ping Xuan, Tiangang Zhang, Ling Gao, Hui Cui, Lan Huang, Yan Wang, Yangkun Cao, Toshiya Nakaguchi, Yuan Fu and Cheng Liao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Analytical Chemistry.

In The Last Decade

Nan Sheng

59 papers receiving 601 citations

Peers

Nan Sheng

CTM

ONCOL

Feiyang Huang China

Danfeng Zhao China

Hao Lin China

Yandong Miao China

Harikrishna Paik India

Kai Yu China

Zhao‐Yue Zhang China

Jiancheng Zhong China

Shouhua Wang China

Junwei Han China

Feiyang Huang China

Nan Sheng

237 ×0.6

165 ×0.5

24 ×0.3

CTM

44 ×0.9

ONCOL

66 ×1.9

Citations per year, relative to Nan Sheng Nan Sheng (= 1×) peers Feiyang Huang

Countries citing papers authored by Nan Sheng

Since Specialization

Citations

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

Fields of papers citing papers by Nan Sheng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nan Sheng

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

Huang, Lan, Y. Shao, Hui Yang, et al.. (2025). PCG-CAM: Enhanced class activation map using principal components of gradients and its applications in brain MRI. Information Sciences. 708. 122046–122046. 1 indexed citations

Wang, Ao, Nan Sheng, Qing Tang, et al.. (2025). Designed amino acid-based deep eutectic solvents as a platform to functionalize biomedical elastomers by polyaddition reactions. Chemical Engineering Journal. 512. 162605–162605. 1 indexed citations

Sun, Keyan, et al.. (2025). Highly elastic phase change fibers for wearable solar-thermal regulation textiles. Journal of Energy Storage. 124. 116885–116885. 2 indexed citations

Su, Yangang, Jie Niu, Qing Lyu, et al.. (2025). Harnessing an MMP-Independent NIR Probe Unveiling the Different Mitochondrial Cristae Changes during Mitophagy and Ferroptosis under STED Microscopy. Analytical Chemistry. 97(5). 2906–2913. 2 indexed citations

Sheng, Nan, et al.. (2024). CircXRN2 accelerates colorectal cancer progression through regulating miR-149-5p/MACC1 axis and EMT. Scientific Reports. 14(1). 2448–2448. 7 indexed citations

Wang, Yan, et al.. (2024). Graph pooling in graph neural networks: methods and their applications in omics studies. Artificial Intelligence Review. 57(11). 6 indexed citations

Wang, Yan, et al.. (2024). Multi-Omics Profiling Unveils the Complexity and Dynamics of Immune Infiltrates in Intrahepatic Cholangiocarcinoma. Biology. 13(10). 816–816. 2 indexed citations

Wang, Xinfei, Lan Huang, Yan Wang, et al.. (2024). A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers. Briefings in Bioinformatics. 25(6). 5 indexed citations

Wang, Yan, Jie Hong, Nan Sheng, et al.. (2024). A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification. IEEE Transactions on NanoBioscience. 23(4). 556–563.

10.

Liu, Qi, Nan Sheng, Zhiwen Zhang, et al.. (2024). Initial nutrient condition determines the recovery speed of quiescent cells in fission yeast. Heliyon. 10(5). e26558–e26558. 1 indexed citations

11.

Mu, Jiuke, Lixue Yang, Nan Sheng, et al.. (2024). Advancing medical devices with soft actuators. 3(1). 100112–100112. 1 indexed citations

12.

Wang, Yan, et al.. (2023). PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence. Genes. 14(7). 1441–1441. 12 indexed citations

13.

Sheng, Nan, Lan Huang, Hao Wang, et al.. (2023). Data resources and computational methods for lncRNA-disease association prediction. Computers in Biology and Medicine. 153. 106527–106527. 25 indexed citations

14.

Yang, Lili, et al.. (2022). MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data. Bioinformatics. 38(19). 4636–4638. 4 indexed citations

15.

Xu, Xiaoqiang, Ping Xuan, Tiangang Zhang, Bingxu Chen, & Nan Sheng. (2021). Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(4). 2294–2304. 7 indexed citations

16.

Moriyama, Taiki, Kenoki Ohuchida, Nan Sheng, et al.. (2021). N-acetyl cysteine induces quiescent-like pancreatic stellate cells from an active state and attenuates cancer-stroma interactions. Journal of Experimental & Clinical Cancer Research. 40(1). 133–133. 17 indexed citations

17.

Xuan, Ping, Ling Gao, Nan Sheng, Tiangang Zhang, & Toshiya Nakaguchi. (2020). Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations. IEEE Journal of Biomedical and Health Informatics. 25(5). 1793–1804. 48 indexed citations

18.

Liu, Yuanlin, Ye Wang, Xiang Shen, et al.. (2020). Down-regulation of lncRNA PCGEM1 inhibits cervical carcinoma by modulating the miR-642a-5p/LGMN axis. Experimental and Molecular Pathology. 117. 104561–104561. 11 indexed citations

19.

Xuan, Ping, et al.. (2019). HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou’s Five-Step Rule. Frontiers in Pharmacology. 10. 1301–1301. 19 indexed citations

20.

Zhang, Qinghong, et al.. (2013). Research of radio wave propagation in forest based on non-uniform mesh parabolic equation. International Symposium on Antennas and Propagation. 2. 838–841. 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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