Pingjian Ding

1.6k total citations
58 papers, 1.2k citations indexed

About

Pingjian Ding is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Pingjian Ding has authored 58 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Molecular Biology, 27 papers in Cancer Research and 19 papers in Computational Theory and Mathematics. Recurrent topics in Pingjian Ding's work include Cancer-related molecular mechanisms research (25 papers), MicroRNA in disease regulation (22 papers) and Computational Drug Discovery Methods (19 papers). Pingjian Ding is often cited by papers focused on Cancer-related molecular mechanisms research (25 papers), MicroRNA in disease regulation (22 papers) and Computational Drug Discovery Methods (19 papers). Pingjian Ding collaborates with scholars based in China, United States and Singapore. Pingjian Ding's co-authors include Jiawei Luo, Cheng Liang, Qiu Xiao, Guanghui Li, Jie Cai, Xiangtao Chen, Rong Xu, Cong Shen, Jiawei Luo and Rui Yin and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Pingjian Ding

53 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pingjian Ding China 19 984 703 227 100 30 58 1.2k
Hai-Cheng Yi China 17 804 0.8× 248 0.4× 362 1.6× 105 1.1× 79 2.6× 39 1000
Junlin Xu China 17 516 0.5× 214 0.3× 187 0.8× 77 0.8× 20 0.7× 53 743
Xiaorui Su China 18 769 0.8× 144 0.2× 549 2.4× 156 1.6× 113 3.8× 43 1.0k
Minjie Mou China 14 664 0.7× 123 0.2× 284 1.3× 43 0.4× 72 2.4× 30 941
Tianyi Zang China 14 610 0.6× 104 0.1× 255 1.1× 120 1.2× 66 2.2× 65 916
Leon Wong China 16 574 0.6× 208 0.3× 232 1.0× 54 0.5× 32 1.1× 34 676
Zhaorong Li China 12 430 0.4× 87 0.1× 171 0.8× 42 0.4× 39 1.3× 17 603
Zhen-Hao Guo China 13 342 0.3× 186 0.3× 124 0.5× 27 0.3× 24 0.8× 26 460
Yanyi Chu China 15 696 0.7× 90 0.1× 383 1.7× 63 0.6× 140 4.7× 20 912
Shuting Jin China 14 507 0.5× 69 0.1× 357 1.6× 88 0.9× 139 4.6× 31 739

Countries citing papers authored by Pingjian Ding

Since Specialization
Citations

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

Fields of papers citing papers by Pingjian Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pingjian Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Pingjian Ding. A scholar is included among the top collaborators of Pingjian Ding 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 Pingjian Ding. Pingjian Ding 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
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3.
Ding, Pingjian, et al.. (2024). Multi-scale DNA language model improves 6 mA binding sites prediction. Computational Biology and Chemistry. 112. 108129–108129. 1 indexed citations
4.
Shen, Cong, et al.. (2024). Curvature-enhanced graph convolutional network for biomolecular interaction prediction. Computational and Structural Biotechnology Journal. 23. 1016–1025. 8 indexed citations
5.
Wu, Ziyu, et al.. (2024). HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations. Computational Biology and Chemistry. 110. 108041–108041.
6.
Zhong, Yichen, et al.. (2023). Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations. Artificial Intelligence in Medicine. 145. 102665–102665. 2 indexed citations
7.
Ding, Pingjian, et al.. (2023). IUPHAR review – Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacological Research. 199. 106960–106960. 4 indexed citations
8.
Shen, Cong, et al.. (2023). Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs. iScience. 26(10). 108020–108020. 3 indexed citations
9.
Li, Ye, et al.. (2023). SENet: A deep learning framework for discriminating super- and typical enhancers by sequence information. Computational Biology and Chemistry. 105. 107905–107905. 12 indexed citations
10.
Chen, Cheng, et al.. (2023). Self-prediction of relations in GO facilitates its quality auditing. Journal of Biomedical Informatics. 144. 104441–104441. 1 indexed citations
11.
Ding, Pingjian & Rong Xu. (2023). Causal association of COVID-19 with brain structure changes: Findings from a non-overlapping 2-sample Mendelian randomization study. Journal of the Neurological Sciences. 454. 120864–120864. 2 indexed citations
12.
Ding, Pingjian, et al.. (2022). Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records. Journal of Biomedical Informatics. 133. 104164–104164. 9 indexed citations
13.
Ding, Pingjian, et al.. (2022). KG-Predict: A knowledge graph computational framework for drug repurposing. Journal of Biomedical Informatics. 132. 104133–104133. 49 indexed citations
14.
Chen, Cheng, et al.. (2022). Improving language model of human genome for DNA–protein binding prediction based on task-specific pre-training. Interdisciplinary Sciences Computational Life Sciences. 15(1). 32–43. 14 indexed citations
15.
Li, Guanghui, Jiawei Luo, Cheng Liang, et al.. (2020). Potential circRNA-disease association prediction using DeepWalk and network consistency projection. Journal of Biomedical Informatics. 112. 103624–103624. 37 indexed citations
16.
Li, Guanghui, et al.. (2020). Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction. Frontiers in Genetics. 11. 89–89. 13 indexed citations
17.
Zhang, Huaxiang, Cheng Liang, Guanghui Li, et al.. (2019). Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction. Molecular Therapy — Nucleic Acids. 17. 414–423. 10 indexed citations
18.
Ding, Pingjian, et al.. (2018). Human disease MiRNA inference by combining target information based on heterogeneous manifolds. Journal of Biomedical Informatics. 80. 26–36. 22 indexed citations
19.
Li, Guanghui, Jiawei Luo, Qiu Xiao, Cheng Liang, & Pingjian Ding. (2018). Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity. Journal of Biomedical Informatics. 82. 169–177. 64 indexed citations
20.
Ding, Pingjian, Jiawei Luo, Qiu Xiao, & Xiangtao Chen. (2016). A path-based measurement for human miRNA functional similarities using miRNA-disease associations. Scientific Reports. 6(1). 32533–32533. 25 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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