Chongle Pan

5.7k total citations
99 papers, 3.8k citations indexed

About

Chongle Pan is a scholar working on Molecular Biology, Ecology and Spectroscopy. According to data from OpenAlex, Chongle Pan has authored 99 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Molecular Biology, 30 papers in Ecology and 24 papers in Spectroscopy. Recurrent topics in Chongle Pan's work include Microbial Community Ecology and Physiology (29 papers), Genomics and Phylogenetic Studies (27 papers) and Advanced Proteomics Techniques and Applications (22 papers). Chongle Pan is often cited by papers focused on Microbial Community Ecology and Physiology (29 papers), Genomics and Phylogenetic Studies (27 papers) and Advanced Proteomics Techniques and Applications (22 papers). Chongle Pan collaborates with scholars based in United States, China and Japan. Chongle Pan's co-authors include Robert L. Hettich, Gregory B. Hurst, Jillian F. Banfield, Karuna Chourey, Nagiza F. Samatova, Nathan C. VerBerkmoes, Tae-Hyuk Ahn, Zhou Li, Rachel M. Adams and David L. Tabb and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Chongle Pan

93 papers receiving 3.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chongle Pan United States 36 2.5k 954 769 602 374 99 3.8k
Manesh Shah United States 37 2.8k 1.1× 1.7k 1.8× 720 0.9× 772 1.3× 344 0.9× 77 4.9k
Dirk Benndorf Germany 34 1.9k 0.8× 795 0.8× 580 0.8× 478 0.8× 255 0.7× 103 3.3k
Samuel Purvine United States 50 4.9k 2.0× 1.0k 1.1× 2.0k 2.6× 911 1.5× 903 2.4× 138 7.8k
Manuel Liebeke Germany 33 2.1k 0.8× 709 0.7× 237 0.3× 216 0.4× 190 0.5× 77 3.4k
Anamika Kothari United States 12 4.4k 1.8× 835 0.9× 141 0.2× 668 1.1× 486 1.3× 16 5.8k
Pallavi Subhraveti United States 10 4.3k 1.8× 841 0.9× 127 0.2× 669 1.1× 490 1.3× 12 5.8k
Quang Ong United States 8 3.5k 1.4× 676 0.7× 109 0.1× 564 0.9× 420 1.1× 10 4.6k
Ron Caspi United States 24 5.3k 2.1× 1.1k 1.1× 155 0.2× 837 1.4× 670 1.8× 37 7.3k
Nathan C. VerBerkmoes United States 48 4.7k 1.9× 2.8k 2.9× 1.4k 1.8× 986 1.6× 408 1.1× 87 7.8k
Carol A. Fulcher United States 12 3.6k 1.5× 692 0.7× 121 0.2× 582 1.0× 409 1.1× 17 4.8k

Countries citing papers authored by Chongle Pan

Since Specialization
Citations

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

Fields of papers citing papers by Chongle Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chongle Pan

This figure shows the co-authorship network connecting the top 25 collaborators of Chongle Pan. A scholar is included among the top collaborators of Chongle Pan 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 Chongle Pan. Chongle Pan 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.
Wei, Jihong, et al.. (2025). Understanding the deterioration mechanism of Xiashu loess in dry-wet cycles. Bulletin of Engineering Geology and the Environment. 84(5). 1 indexed citations
2.
3.
Brandt, Peter, Chen Wang, Paul P. Calle, et al.. (2025). Deep Learning for Autonomous Surgical Guidance Using 3‐Dimensional Images From Forward‐Viewing Endoscopic Optical Coherence Tomography. Journal of Biophotonics. 18(11). e202500181–e202500181.
4.
Pan, Chongle, et al.. (2025). Unified modeling language code generation from diagram images using multimodal large language models. Machine Learning with Applications. 20. 100660–100660.
5.
Hunt, Kristopher A., Xuanyu Tao, Ralph S. Tanner, et al.. (2025). Higher-order microbial interactions revealed by comparative metabolic modeling of synthetic communities with varying species composition. ISME Communications. 5(1). ycaf142–ycaf142.
6.
Wang, Chen, Qinghao Zhang, Paul P. Calle, et al.. (2024). Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning. SHILAP Revista de lepidopterología. 3(1). 107–107. 1 indexed citations
7.
Calle, Paul P., Emily T. Hébert, Darla E. Kendzor, et al.. (2024). Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians’ Evaluation of Large Language Models for Smoking Cessation Interventions. PubMed. 2024. 1–16. 4 indexed citations
8.
Xiong, Yi, et al.. (2024). CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing. Bioinformatics Advances. 4(1). vbae024–vbae024.
9.
10.
Cui, Yuning, et al.. (2023). Bayesian inference for survival prediction of childhood Leukemia. Computers in Biology and Medicine. 156. 106713–106713. 3 indexed citations
11.
Xu, Tao, Xuanyu Tao, Megan L. Kempher, et al.. (2023). Functional and structural diversification of incomplete phosphotransferase system in cellulose-degrading clostridia. The ISME Journal. 17(6). 823–835. 15 indexed citations
12.
Peng, Mugen, Lauren Michelle Lui, Torben Nielsen, et al.. (2022). Genomic Features and Pervasive Negative Selection in Rhodanobacter Strains Isolated from Nitrate and Heavy Metal Contaminated Aquifer. Microbiology Spectrum. 10(1). e0259121–e0259121. 18 indexed citations
13.
Zhou, Li, Samuel Bryson, Robert L. Hettich, et al.. (2021). Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proceedings of the National Academy of Sciences. 118(41). 52 indexed citations
14.
Diamond, Spencer, Peter Andeer, Li Zhou, et al.. (2019). Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nature Microbiology. 4(8). 1356–1367. 145 indexed citations
15.
Guo, Xuan, Zhou Li, Qiuming Yao, et al.. (2017). Sipros Ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics. 34(5). 795–802. 23 indexed citations
16.
Kappler, Ulrike, Karen W. Davenport, Scott A. Beatson, et al.. (2016). Complete genome sequence of the haloalkaliphilic, obligately chemolithoautotrophic thiosulfate and sulfide-oxidizing γ-proteobacterium Thioalkalimicrobium cyclicum type strain ALM 1 (DSM 14477T). Standards in Genomic Sciences. 11(1). 38–38. 6 indexed citations
17.
Zhou, Li, Yingfeng Wang, Qiuming Yao, et al.. (2014). Diverse and divergent protein post-translational modifications in two growth stages of a natural microbial community. Nature Communications. 5(1). 4405–4405. 46 indexed citations
18.
Mueller, Ryan & Chongle Pan. (2013). Sample Handling and Mass Spectrometry for Microbial Metaproteomic Analyses. Methods in enzymology on CD-ROM/Methods in enzymology. 531. 289–303. 9 indexed citations
19.
20.
Yan, Bo, Chongle Pan, Victor Olman, Robert L. Hettich, & Ying Xu. (2004). A graph-theoretic approach for the separation of b and y ions in tandem mass spectra. Bioinformatics. 21(5). 563–574. 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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