Ickwon Choi

1.1k total citations
7 papers, 513 citations indexed

About

Ickwon Choi is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Ickwon Choi has authored 7 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Immunology. Recurrent topics in Ickwon Choi's work include Monoclonal and Polyclonal Antibodies Research (3 papers), HIV Research and Treatment (2 papers) and Immune Cell Function and Interaction (2 papers). Ickwon Choi is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (3 papers), HIV Research and Treatment (2 papers) and Immune Cell Function and Interaction (2 papers). Ickwon Choi collaborates with scholars based in United States, South Korea and United Kingdom. Ickwon Choi's co-authors include Galit Alter, Margaret E. Ackerman, Chris Bailey‐Kellogg, Anna F. Licht, Anne‐Sophie Dugast, Austin W. Boesch, Kavitha Baruah, Peter A. Nigrović, Max Crispin and Xiaojie Yu and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Virology and PLoS Computational Biology.

In The Last Decade

Ickwon Choi

7 papers receiving 509 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ickwon Choi United States	6	292	270	216	156	69	7	513
Marie Zöller Germany	4	341 1.2×	152 0.6×	166 0.8×	169 1.1×	68 1.0×	5	544
Sylvie Schmidt France	15	348 1.2×	155 0.6×	176 0.8×	360 2.3×	110 1.6×	38	657
Frederick H. Jaeger United States	11	185 0.6×	106 0.4×	157 0.7×	259 1.7×	79 1.1×	12	423
Juan Carlos Aldave Becerra United States	10	201 0.7×	56 0.2×	78 0.4×	147 0.9×	83 1.2×	19	384
Anne B. Kristensen Australia	10	342 1.2×	181 0.7×	203 0.9×	165 1.1×	117 1.7×	15	539
Caroline Eden United States	10	326 1.1×	224 0.8×	202 0.9×	460 2.9×	96 1.4×	14	691
Cassie Liu United States	8	343 1.2×	162 0.6×	195 0.9×	397 2.5×	108 1.6×	13	683
Thomas Decoville France	12	350 1.2×	111 0.4×	94 0.4×	354 2.3×	99 1.4×	21	507
Ashraf S. Yousif United States	9	146 0.5×	74 0.3×	157 0.7×	45 0.3×	67 1.0×	21	351
Carrie Moore United States	13	356 1.2×	126 0.5×	435 2.0×	46 0.3×	98 1.4×	27	768

Countries citing papers authored by Ickwon Choi

Since Specialization

Citations

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

Fields of papers citing papers by Ickwon Choi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ickwon Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Ickwon Choi. A scholar is included among the top collaborators of Ickwon Choi 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 Ickwon Choi. Ickwon Choi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

Oh, Ki‐Kwang, Ickwon Choi, Haripriya Gupta, et al.. (2022). New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study. Artificial Cells Nanomedicine and Biotechnology. 51(1). 1–12. 7 indexed citations

Choi, Ickwon, Amy W. Chung, Todd J. Suscovich, et al.. (2015). Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees. PLoS Computational Biology. 11(4). e1004185–e1004185. 32 indexed citations

Ackerman, Margaret E., Max Crispin, Xiaojie Yu, et al.. (2013). Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity. Journal of Clinical Investigation. 123(5). 2183–2192. 279 indexed citations

Licht, Anna F., Anne‐Sophie Dugast, Todd J. Suscovich, et al.. (2013). Divergent Antibody Subclass and Specificity Profiles but Not Protective HLA-B Alleles Are Associated with Variable Antibody Effector Function among HIV-1 Controllers. Journal of Virology. 88(5). 2799–2809. 34 indexed citations

Choi, Ickwon, Michael W. Kattan, Brian J. Wells, & Changhong Yu. (2012). A Hybrid Approach to Survival Model Building Using Integration of Clinical and Molecular Information in Censored Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(4). 1091–1105. 3 indexed citations

Brown, Eric P., Anna F. Licht, Anne‐Sophie Dugast, et al.. (2012). High-throughput, multiplexed IgG subclassing of antigen-specific antibodies from clinical samples. Journal of Immunological Methods. 386(1-2). 117–123. 124 indexed citations

Choi, Ickwon, Brian J. Wells, Changhong Yu, & Michael W. Kattan. (2011). An empirical approach to model selection through validation for censored survival data. Journal of Biomedical Informatics. 44(4). 595–606. 34 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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