Weigong Ge

6.7k total citations
37 papers, 1.6k citations indexed

About

Weigong Ge is a scholar working on Computational Theory and Mathematics, Molecular Biology and Genetics. According to data from OpenAlex, Weigong Ge has authored 37 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 15 papers in Molecular Biology and 11 papers in Genetics. Recurrent topics in Weigong Ge's work include Computational Drug Discovery Methods (17 papers), Estrogen and related hormone effects (6 papers) and Genetic Associations and Epidemiology (5 papers). Weigong Ge is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Estrogen and related hormone effects (6 papers) and Genetic Associations and Epidemiology (5 papers). Weigong Ge collaborates with scholars based in United States, China and Germany. Weigong Ge's co-authors include Weida Tong, Huixiao Hong, Roger Perkins, Wen Zou, Zhenqiang Su, Hong Fang, Sugunadevi Sakkiah, Wenjing Guo, Bohu Pan and James J. Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Molecular Sciences and American Journal Of Pathology.

In The Last Decade

Weigong Ge

37 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Weigong Ge United States 18 477 397 266 229 126 37 1.6k
Nancy Baker United States 19 570 1.2× 386 1.0× 615 2.3× 97 0.4× 52 0.4× 50 1.8k
Xiao Li China 30 736 1.5× 539 1.4× 85 0.3× 89 0.4× 131 1.0× 169 2.8k
Mohammed Talbi Morocco 25 405 0.8× 77 0.2× 230 0.9× 444 1.9× 97 0.8× 159 2.4k
Rob Stierum Netherlands 25 1.2k 2.5× 136 0.3× 269 1.0× 216 0.9× 189 1.5× 63 2.2k
Alberto Fernández Spain 21 261 0.5× 222 0.6× 131 0.5× 40 0.2× 21 0.2× 56 1.8k
Judith C. Madden United Kingdom 32 627 1.3× 1.2k 3.1× 740 2.8× 35 0.2× 194 1.5× 104 3.0k
Todd M. Martin United States 23 665 1.4× 772 1.9× 421 1.6× 34 0.1× 56 0.4× 43 2.1k
Jinghua Zhao China 23 884 1.9× 339 0.9× 58 0.2× 70 0.3× 76 0.6× 54 2.5k
Yue Wang China 26 1.0k 2.1× 81 0.2× 43 0.2× 351 1.5× 58 0.5× 135 3.1k

Countries citing papers authored by Weigong Ge

Since Specialization
Citations

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

Fields of papers citing papers by Weigong Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weigong Ge

This figure shows the co-authorship network connecting the top 25 collaborators of Weigong Ge. A scholar is included among the top collaborators of Weigong Ge 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 Weigong Ge. Weigong Ge 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.
Ge, Weigong, Beverly Lyn‐Cook, Huixiao Hong, et al.. (2025). AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women. Experimental Biology and Medicine. 250. 10389–10389. 6 indexed citations
2.
Liu, Jie, Zoe Li, Fan Dong, et al.. (2025). Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques. Experimental Biology and Medicine. 250. 10359–10359. 1 indexed citations
3.
Le, Huyen, Stephen Harris, Hong Fang, et al.. (2024). RxNorm for drug name normalization: a case study of prescription opioids in the FDA adverse events reporting system. SHILAP Revista de lepidopterología. 3. 1328613–1328613. 3 indexed citations
4.
Le, Huyen, Huixiao Hong, Weigong Ge, et al.. (2023). A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database. Experimental Biology and Medicine. 248(21). 1944–1951. 4 indexed citations
5.
Guo, Wenjing, Jie Liu, Fan Dong, et al.. (2022). Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. Nanomaterials. 12(19). 3376–3376. 28 indexed citations
6.
Pan, Bohu, Rebecca Kusko, Wenming Xiao, et al.. (2019). Similarities and differences between variants called with human reference genome HG19 or HG38. BMC Bioinformatics. 20(S2). 101–101. 42 indexed citations
7.
Sakkiah, Sugunadevi, Rebecca Kusko, Bohu Pan, et al.. (2018). Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations. Frontiers in Pharmacology. 9. 492–492. 33 indexed citations
8.
Ye, Hao, Heng Luo, Hui Wen Ng, et al.. (2016). Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data. Environment International. 89-90. 81–92. 6 indexed citations
9.
Zhang, Jie, Kan He, Lining Cai, et al.. (2016). Inhibition of bile salt transport by drugs associated with liver injury in primary hepatocytes from human, monkey, dog, rat, and mouse. Chemico-Biological Interactions. 255. 45–54. 32 indexed citations
10.
Shu, Mao, Hui Wen Ng, Michael S. Orr, et al.. (2016). Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human <i>α</i>4<i>β</i>2 Nicotinic Acetylcholine Receptor. Journal of Biomedical Science and Engineering. 9(1). 41–100. 5 indexed citations
11.
Hong, Huixiao, Jie Shen, Hui Wen Ng, et al.. (2016). A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals. International Journal of Environmental Research and Public Health. 13(4). 372–372. 12 indexed citations
12.
Ng, Hui Wen, Mao Shu, Heng Luo, et al.. (2015). Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium. Journal of Genetics. 94(4). 731–740. 12 indexed citations
13.
Zhao, Weizhong, James J. Chen, Roger Perkins, et al.. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 16(S13). S8–S8. 254 indexed citations
14.
Shen, Jie, Lei Xu, Hong Fang, et al.. (2013). EADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity. Toxicological Sciences. 135(2). 277–291. 56 indexed citations
15.
Wang, Yuping, Zhichao Liu, Stephen Harris, et al.. (2013). A Unifying Ontology to Integrate Histological and Clinical Observations for Drug-Induced Liver Injury. American Journal Of Pathology. 182(4). 1180–1187. 16 indexed citations
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Ding, Yijun, Minjun Chen, Zhichao Liu, et al.. (2012). atBioNet– an integrated network analysis tool for genomics and biomarker discovery. BMC Genomics. 13(1). 325–325. 30 indexed citations
19.
Hong, Huixiao, Zhenqiang Su, Weigong Ge, et al.. (2010). Evaluating variations of genotype calling: a potential source of spurious associations in genome-wide association studies. Journal of Genetics. 89(1). 55–64. 9 indexed citations
20.
Hong, Huixiao, Zhenqiang Su, Weigong Ge, et al.. (2008). Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples. BMC Bioinformatics. 9(S9). S17–S17. 61 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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