Jing Tang

13.6k total citations · 7 hit papers
206 papers, 7.5k citations indexed

About

Jing Tang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Jing Tang has authored 206 papers receiving a total of 7.5k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Molecular Biology, 46 papers in Computational Theory and Mathematics and 24 papers in Artificial Intelligence. Recurrent topics in Jing Tang's work include Computational Drug Discovery Methods (45 papers), Bioinformatics and Genomic Networks (32 papers) and Pharmacogenetics and Drug Metabolism (10 papers). Jing Tang is often cited by papers focused on Computational Drug Discovery Methods (45 papers), Bioinformatics and Genomic Networks (32 papers) and Pharmacogenetics and Drug Metabolism (10 papers). Jing Tang collaborates with scholars based in China, Finland and United States. Jing Tang's co-authors include Tero Aittokallio, Krister Wennerberg, Jukka Corander, Pekka Marttinen, Jukka Sirén, Bhagwan Yadav, Agnieszka Szwajda, Liye He, Sushil Kumar Shakyawar and Wenyu Wang and has published in prestigious journals such as Science, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Jing Tang

185 papers receiving 7.4k citations

Hit Papers

Enhanced Bayesian modelling in BAPS software for learning... 2008 2026 2014 2020 2008 2015 2014 2014 2017 200 400 600
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.

  1. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations
    2008 695 citations Jukka Corander, Jing Tang et al. BMC Bioinformatics profile →
  2. Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model
    2015 506 citations Krister Wennerberg, Tero Aittokallio et al. profile →
  3. Toward more realistic drug-target interaction predictions
    2014 379 citations Jing Tang, Tero Aittokallio et al. Briefings in Bioinformatics profile →
  4. Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
    2014 377 citations Jing Tang, Krister Wennerberg et al. profile →
  5. SynergyFinder: a web application for analyzing drug combination dose–response matrix data
    2017 353 citations Liye He, Tero Aittokallio et al. Bioinformatics profile →
  6. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products
    2015 346 citations Jing Tang, Krister Wennerberg et al. profile →
  7. SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets
    2022 320 citations Shuyu Zheng, Wenyu Wang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jing Tang China 41 4.1k 2.0k 774 678 487 206 7.5k
James Inglese United States 59 9.9k 2.4× 1.8k 0.9× 602 0.8× 946 1.4× 626 1.3× 192 13.8k
Tero Aittokallio Finland 54 6.3k 1.5× 2.3k 1.1× 574 0.7× 1.4k 2.0× 405 0.8× 244 10.2k
Wei Zheng United States 57 6.0k 1.5× 1.3k 0.6× 615 0.8× 698 1.0× 651 1.3× 382 12.6k
Ajay N. Jain United States 43 5.4k 1.3× 3.0k 1.4× 1.5k 2.0× 1.1k 1.6× 711 1.5× 116 8.8k
Yoshihiro Yamanishi Japan 33 6.7k 1.6× 3.1k 1.5× 661 0.9× 196 0.3× 691 1.4× 124 10.0k
Anne‐Claude Gavin Germany 41 5.3k 1.3× 1.1k 0.6× 432 0.6× 291 0.4× 236 0.5× 91 6.7k
Kenji Mizuguchi Japan 42 5.0k 1.2× 668 0.3× 692 0.9× 501 0.7× 195 0.4× 232 7.5k
Feng Zhu China 58 8.5k 2.1× 3.8k 1.8× 454 0.6× 891 1.3× 866 1.8× 291 13.3k
Jianyi Yang China 37 6.3k 1.5× 1.5k 0.8× 536 0.7× 286 0.4× 288 0.6× 114 8.6k
Yu‐Dong Cai China 64 11.8k 2.9× 2.4k 1.2× 438 0.6× 674 1.0× 287 0.6× 491 15.6k

Countries citing papers authored by Jing Tang

Since Specialization
Citations

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

Fields of papers citing papers by Jing Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jing Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Jing Tang. A scholar is included among the top collaborators of Jing Tang 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 Jing Tang. Jing Tang 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.
2.
Zhang, Zhehao, Yiwei Liu, Zhan Gao, et al.. (2025). Factors influencing cardiovascular patients to complete phase II cardiac rehabilitation – a qualitative study. BMC Cardiovascular Disorders. 25(1). 688–688.
3.
Tang, Jing, et al.. (2024). Towards a semi-supervised ensemble clustering framework with flexible weighting mechanism and constraints information. Engineering Applications of Artificial Intelligence. 136. 108976–108976. 6 indexed citations
4.
Kucińska, Małgorzata, Jing Tang, Natalia Lisiak, et al.. (2024). The combination therapy using tyrosine kinase receptors inhibitors and repurposed drugs to target patient-derived glioblastoma stem cells. Biomedicine & Pharmacotherapy. 176. 116892–116892. 4 indexed citations
5.
6.
Wang, Mengzhu, et al.. (2024). Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization. Proceedings of the AAAI Conference on Artificial Intelligence. 38(13). 15091–15099. 4 indexed citations
7.
Li, Rongbin, et al.. (2024). SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations. BMC Bioinformatics. 25(1). 250–250. 3 indexed citations
8.
Amiryousefi, Ali, et al.. (2022). The ENDS of assumptions: an online tool for the epistemic non-parametric drug–response scoring. Bioinformatics. 38(11). 3132–3133. 1 indexed citations
9.
Aldahdooh, Jehad, Markus Vähä‐Koskela, Jing Tang, & Ziaurrehman Tanoli. (2022). Using BERT to identify drug-target interactions from whole PubMed. BMC Bioinformatics. 23(1). 3–18. 11 indexed citations
10.
Fan, Xinyue, Yanyan Yan, Orel Vé, et al.. (2021). Application of microfluidic chips in anticancer drug screening. SHILAP Revista de lepidopterología. 22(3). 302–314. 16 indexed citations
11.
Zheng, Shuyu, Jehad Aldahdooh, Tolou Shadbahr, et al.. (2021). DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal. Nucleic Acids Research. 49(W1). W174–W184. 65 indexed citations
12.
Tanoli, Ziaurrehman, Jehad Aldahdooh, Yinyin Wang, et al.. (2021). Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments. Briefings in Bioinformatics. 23(1). 8 indexed citations
13.
Wang, Yinyin, et al.. (2021). Network-based modeling of herb combinations in traditional Chinese medicine. Briefings in Bioinformatics. 22(5). 99 indexed citations
14.
Kim, Yejin, et al.. (2020). Anticancer drug synergy prediction in understudied tissues using transfer learning. Journal of the American Medical Informatics Association. 28(1). 42–51. 61 indexed citations
15.
He, Liye, Jing Tang, Emma Andersson, et al.. (2018). Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients. Cancer Research. 78(9). 2407–2418. 55 indexed citations
16.
He, Liye, Krister Wennerberg, Tero Aittokallio, & Jing Tang. (2015). TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples. Bioinformatics. 31(11). 1866–1868. 11 indexed citations
17.
Wang, Xiaohan, Jing Tang, Lijuan Xing, et al.. (2010). Interaction of MAGED1 with nuclear receptors affects circadian clock function. The EMBO Journal. 29(8). 1389–1400. 33 indexed citations
18.
Hanage, William P., Christophe Fraser, Jing Tang, Thomas R. Connor, & Jukka Corander. (2009). Hyper-Recombination, Diversity, and Antibiotic Resistance in Pneumococcus. Science. 324(5933). 1454–1457. 133 indexed citations
19.
Tang, Jing, et al.. (2007). T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data. Statistical Applications in Genetics and Molecular Biology. 6(1). Article30–Article30. 8 indexed citations
20.
Guo, Ke, Jie Li, Haihe Wang, et al.. (2006). PRL-3 Initiates Tumor Angiogenesis by Recruiting Endothelial Cells In vitro and In vivo. Cancer Research. 66(19). 9625–9635. 77 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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