Xiwei Tang

673 total citations
26 papers, 414 citations indexed

About

Xiwei Tang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Xiwei Tang has authored 26 papers receiving a total of 414 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 4 papers in Artificial Intelligence. Recurrent topics in Xiwei Tang's work include Computational Drug Discovery Methods (7 papers), Bayesian Methods and Mixture Models (4 papers) and Statistical Methods and Inference (4 papers). Xiwei Tang is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Bayesian Methods and Mixture Models (4 papers) and Statistical Methods and Inference (4 papers). Xiwei Tang collaborates with scholars based in United States, China and India. Xiwei Tang's co-authors include Jiancheng Zhong, Yi Pan, Jianxin Wang, Annie Qu, Manjari Kiran, Ajay Chatrath, Anindya Dutta, Daniel M. Keenan, Xuan Bi and Fei Xue and has published in prestigious journals such as Journal of the American Statistical Association, International Journal of Molecular Sciences and BMC Bioinformatics.

In The Last Decade

Xiwei Tang

24 papers receiving 407 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiwei Tang United States 9 198 81 68 56 45 26 414
Zi Yang United States 11 270 1.4× 43 0.5× 201 3.0× 27 0.5× 303 6.7× 27 676
Suleiman A. Khan Finland 12 255 1.3× 150 1.9× 50 0.7× 47 0.8× 19 0.4× 24 386
Rong Ma United States 12 180 0.9× 12 0.1× 59 0.9× 80 1.4× 39 0.9× 34 499
Songjian Lu United States 15 292 1.5× 319 3.9× 133 2.0× 67 1.2× 25 0.6× 34 717
Ben Morris United States 13 308 1.6× 59 0.7× 47 0.7× 92 1.6× 22 0.5× 34 703
Jorge M. Arevalillo Spain 11 85 0.4× 12 0.1× 58 0.9× 32 0.6× 42 0.9× 32 287
Tzu-Yi Chen United States 14 144 0.7× 31 0.4× 57 0.8× 20 0.4× 17 0.4× 38 436
Laura Cantini France 12 447 2.3× 48 0.6× 127 1.9× 44 0.8× 34 0.8× 27 589
Nimrod Rappoport Israel 7 497 2.5× 18 0.2× 128 1.9× 63 1.1× 68 1.5× 8 630
Przemysław Waliszewski Poland 16 216 1.1× 34 0.4× 41 0.6× 48 0.9× 89 2.0× 40 562

Countries citing papers authored by Xiwei Tang

Since Specialization
Citations

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

Fields of papers citing papers by Xiwei Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiwei Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiwei Tang. A scholar is included among the top collaborators of Xiwei 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 Xiwei Tang. Xiwei 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.
Li, Wen‐Jun, et al.. (2025). MDG-DDI: multi-feature drug graph for drug-drug interaction prediction. BMC Bioinformatics. 26(1). 268–268.
2.
Tang, Xiwei, et al.. (2024). MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction. Methods. 231. 1–7. 3 indexed citations
3.
Tang, Xiwei, Yiqiang Zhou, Mengyun Yang, & Wenjun Li. (2024). TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks. IEEE Transactions on NanoBioscience. 23(4). 572–578. 5 indexed citations
4.
5.
Tang, Xiwei, et al.. (2022). Genetic Evidence for a Causal Relationship between Hyperlipidemia and Type 2 Diabetes in Mice. International Journal of Molecular Sciences. 23(11). 6184–6184. 7 indexed citations
6.
Xue, Fei, Xiwei Tang, Grace S. Kim, et al.. (2022). Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in a Predominantly African American Cohort. Journal of the American Statistical Association. 117(540). 1669–1683. 8 indexed citations
7.
Tang, Xiwei, et al.. (2021). Hyperlipidemia Influences the Accuracy of Glucometer-Measured Blood Glucose Concentrations in Genetically Diverse Mice. The American Journal of the Medical Sciences. 362(3). 297–302. 6 indexed citations
8.
Deng, Yujia, Xiwei Tang, & Annie Qu. (2021). Correlation Tensor Decomposition and Its Application in Spatial Imaging Data. Journal of the American Statistical Association. 118(541). 440–456. 7 indexed citations
9.
Tang, Xiwei & Lexin Li. (2021). Multivariate Temporal Point Process Regression. Journal of the American Statistical Association. 118(542). 830–845. 4 indexed citations
10.
Tang, Xiwei, Fei Xue, & Annie Qu. (2020). Individualized Multidirectional Variable Selection. Journal of the American Statistical Association. 116(535). 1280–1296. 24 indexed citations
11.
Chatrath, Ajay, Shashi Kiran, Zhangli Su, et al.. (2020). The pan-cancer landscape of prognostic germline variants in 10,582 patients. Genome Medicine. 12(1). 15–15. 23 indexed citations
12.
Boyle‐Vavra, Susan, et al.. (2019). Carriage of Methicillin-resistant Staphylococcus aureus in a Colony of Rhesus (Macaca mulatta) and Cynomolgus (Macaca fascicularis) Macaques. Comparative Medicine. 69(4). 311–320. 7 indexed citations
13.
Tang, Xiwei, Xuan Bi, & Annie Qu. (2019). Individualized Multilayer Tensor Learning With an Application in Imaging Analysis. Journal of the American Statistical Association. 115(530). 836–851. 14 indexed citations
14.
Tang, Xiwei, et al.. (2019). Longitudinal Principal Component Analysis With an Application to Marketing Data. Journal of Computational and Graphical Statistics. 29(2). 335–350. 6 indexed citations
15.
Kiran, Manjari, Ajay Chatrath, Xiwei Tang, Daniel M. Keenan, & Anindya Dutta. (2018). A Prognostic Signature for Lower Grade Gliomas Based on Expression of Long Non-Coding RNAs. Molecular Neurobiology. 56(7). 4786–4798. 70 indexed citations
16.
Tang, Xiwei, et al.. (2017). Radioactive Seed Localization Versus Wire Localization for Nonpalpable Breast Lesions: A Two-Year Initial Experience at a Large Community Hospital. Annals of Surgical Oncology. 25(1). 131–136. 12 indexed citations
17.
Peng, Wei, et al.. (2016). Identification of essential proteins via the network topology feature and subcellular localisation. International Journal of Data Mining and Bioinformatics. 16(4). 328–328. 1 indexed citations
18.
Tang, Xiwei & Annie Qu. (2015). Mixture Modeling for Longitudinal Data. Journal of Computational and Graphical Statistics. 25(4). 1117–1137. 18 indexed citations
19.
Tang, Xiwei, Jianxin Wang, Jiancheng Zhong, & Yi Pan. (2014). Predicting Essential Proteins Based on Weighted Degree Centrality. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 11(2). 407–418. 153 indexed citations
20.
Teng, Yincheng, et al.. (2003). [Relationship between left ventricular diastolic function and plasma brain natriuretic peptide concentration during severe pregnancy-induced hypertension syndrome].. PubMed. 83(8). 662–5. 2 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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