Young Hwan Chang

4.6k total citations
105 papers, 1.4k citations indexed

About

Young Hwan Chang is a scholar working on Molecular Biology, Oncology and Biophysics. According to data from OpenAlex, Young Hwan Chang has authored 105 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 26 papers in Oncology and 24 papers in Biophysics. Recurrent topics in Young Hwan Chang's work include Single-cell and spatial transcriptomics (23 papers), Cell Image Analysis Techniques (19 papers) and Cancer Cells and Metastasis (12 papers). Young Hwan Chang is often cited by papers focused on Single-cell and spatial transcriptomics (23 papers), Cell Image Analysis Techniques (19 papers) and Cancer Cells and Metastasis (12 papers). Young Hwan Chang collaborates with scholars based in United States, South Korea and United Kingdom. Young Hwan Chang's co-authors include Claire J. Tomlin, Joe W. Gray, Qie Hu, Guillaume Thibault, Amanda W. Lund, Julia Femel, Dariush Fooladivanda, Takahiro Tsujikawa, Erik A. Burlingame and Christopher P. Loo and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Experimental Medicine and SHILAP Revista de lepidopterología.

In The Last Decade

Young Hwan Chang

99 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young Hwan Chang United States 19 492 423 246 209 189 105 1.4k
Biao Luo China 17 328 0.7× 1.6k 3.9× 90 0.4× 184 0.9× 215 1.1× 31 2.5k
Hongzhi Li China 24 147 0.3× 599 1.4× 100 0.4× 91 0.4× 60 0.3× 92 2.0k
Paul G. O’Reilly United Kingdom 19 286 0.6× 364 0.9× 83 0.3× 41 0.2× 87 0.5× 51 1.0k
Rongshan Yu Singapore 18 185 0.4× 206 0.5× 45 0.2× 83 0.4× 78 0.4× 103 1.2k
Paul S. Andrews United Kingdom 17 132 0.3× 526 1.2× 70 0.3× 54 0.3× 146 0.8× 52 1.1k
Alessandro Bevilacqua Italy 22 480 1.0× 431 1.0× 793 3.2× 17 0.1× 71 0.4× 118 2.4k
Tao Peng China 28 368 0.7× 1.8k 4.3× 123 0.5× 34 0.2× 213 1.1× 104 2.8k
Xiao Yang China 17 185 0.4× 264 0.6× 338 1.4× 78 0.4× 123 0.7× 125 1.4k
Xiaoming Zheng United States 18 361 0.7× 320 0.8× 179 0.7× 120 0.6× 43 0.2× 55 2.0k
Zeynep H. Gümüş United States 24 408 0.8× 1.0k 2.4× 53 0.2× 376 1.8× 135 0.7× 55 2.4k

Countries citing papers authored by Young Hwan Chang

Since Specialization
Citations

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

Fields of papers citing papers by Young Hwan Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young Hwan Chang. 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 Young Hwan Chang. The network helps show where Young Hwan Chang may publish in the future.

Co-authorship network of co-authors of Young Hwan Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Young Hwan Chang. A scholar is included among the top collaborators of Young Hwan Chang 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 Young Hwan Chang. Young Hwan Chang 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.
Parappilly, Michael, Emile Latour, Lei Wang, et al.. (2024). Circulating Neoplastic-Immune Hybrid Cells Are Biomarkers of Occult Metastasis and Treatment Response in Pancreatic Cancer. Cancers. 16(21). 3650–3650.
2.
Jones, Jocelyn, et al.. (2024). Ultra high content analyses of circulating and tumor associated hybrid cells reveal phenotypic heterogeneity. Scientific Reports. 14(1). 7350–7350. 5 indexed citations
3.
Parappilly, Michael, Brett S. Walker, Alice Fung, et al.. (2024). Exploratory Analyses of Circulating Neoplastic-Immune Hybrid Cells as Prognostic Biomarkers in Advanced Intrahepatic Cholangiocarcinoma. International Journal of Molecular Sciences. 25(17). 9198–9198. 1 indexed citations
4.
Mills, Gordon B., et al.. (2024). MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation. Communications Biology. 7(1). 409–409. 3 indexed citations
5.
Thibault, Guillaume, et al.. (2024). AxonFinder: Automated segmentation of tumor innervating neuronal fibers. Heliyon. 11(1). e41209–e41209. 1 indexed citations
6.
Kim, Eun Na, Dario Bressan, Monika Tripathi, et al.. (2023). Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole-slide imaging and accurate segmentation. Cell Reports Methods. 3(10). 100595–100595. 11 indexed citations
7.
Steele, Maria M., Ian Dryg, Dhaarini Murugan, et al.. (2023). T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control. Nature Immunology. 24(4). 664–675. 71 indexed citations
8.
Riesterer, Jessica L., Erin Stempinski, Clàudia López, et al.. (2023). Large-Scale Electron Microscopy to Find Nanoscale Detail in Cancer. Microscopy and Microanalysis. 29(Supplement_1). 1078–1079. 2 indexed citations
9.
Ternes, Luke, Mark Dane, Sean M. Gross, et al.. (2022). A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis. Communications Biology. 5(1). 255–255. 26 indexed citations
10.
Asadpoure, Alireza, et al.. (2022). Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model. PLoS Computational Biology. 18(3). e1009953–e1009953. 7 indexed citations
11.
Parappilly, Michael, Thomas L. Sutton, Summer L. Gibbs, et al.. (2022). Circulating Neoplastic-Immune Hybrid Cells Predict Metastatic Progression in Uveal Melanoma. Cancers. 14(19). 4617–4617. 13 indexed citations
12.
Abdel‐Rahman, Mohamed H., Alireza Asadpoure, Colleen M. Cebulla, et al.. (2021). A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration. Journal of Personalized Medicine. 11(10). 1031–1031. 23 indexed citations
13.
Fooladivanda, Dariush, Qie Hu, & Young Hwan Chang. (2021). Secure Dynamic State Estimation for Cyber Security of AC Microgrids. 2. 3 indexed citations
14.
Trinh, Anne, Carlos R. Gil Del Alcazar, Sachet A. Shukla, et al.. (2020). Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System. Molecular Cancer Research. 19(4). 623–635. 24 indexed citations
15.
Jindal, Sonali, Tiffany L. Chan, Shamilene Sivagnanam, et al.. (2020). Loss of myoepithelial calponin‐1 characterizes high‐risk ductal carcinoma in situ cases, which are further stratified by T cell composition. Molecular Carcinogenesis. 59(7). 701–712. 11 indexed citations
16.
Lane, Ryan S., Julia Femel, Christopher P. Loo, et al.. (2018). IFNγ-activated dermal lymphatic vessels inhibit cytotoxic T cells in melanoma and inflamed skin. The Journal of Experimental Medicine. 215(12). 3057–3074. 126 indexed citations
17.
18.
Hu, Qie, Young Hwan Chang, & Claire J. Tomlin. (2016). Secure Estimation for Unmanned Aerial Vehicles against Adversarial Cyber Attacks.. arXiv (Cornell University). 2 indexed citations
19.
Chang, Young Hwan, Joe W. Gray, & Claire J. Tomlin. (2012). Optimization-based Inference for Temporally Evolving Networks with Applications in Biology. Journal of Computational Biology. 19(12). 1307–1323. 4 indexed citations
20.
Chang, Young Hwan, Joe W. Gray, & Claire J. Tomlin. (2011). Optimization-based inference for temporally evolving Boolean networks with applications in biology. 11. 4129–4134. 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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