Ivan Chang

8.8k total citations · 1 hit paper
15 papers, 4.1k citations indexed

About

Ivan Chang is a scholar working on Molecular Biology, Artificial Intelligence and Biophysics. According to data from OpenAlex, Ivan Chang has authored 15 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Artificial Intelligence and 2 papers in Biophysics. Recurrent topics in Ivan Chang's work include Single-cell and spatial transcriptomics (3 papers), Mitochondrial Function and Pathology (2 papers) and Microbial Metabolic Engineering and Bioproduction (2 papers). Ivan Chang is often cited by papers focused on Single-cell and spatial transcriptomics (3 papers), Mitochondrial Function and Pathology (2 papers) and Microbial Metabolic Engineering and Bioproduction (2 papers). Ivan Chang collaborates with scholars based in United States, France and Taiwan. Ivan Chang's co-authors include Raúl Ramos, Chen‐Hsiang Kuan, Christian F. Guerrero‐Juarez, Qing Nie, Peggy Myung, Suoqin Jin, Lihua Zhang, Maksim V. Plikus, Suzanne Sandmeyer and Jay D. Keasling and has published in prestigious journals such as Nature Communications, Bioinformatics and PLoS ONE.

In The Last Decade

Ivan Chang

15 papers receiving 4.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Inference and analysis of cell-cell communication using CellChat

2021 3.8k citations Suoqin Jin, Christian F. Guerrero‐Juarez et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ivan Chang United States	9	2.3k	1.5k	844	643	609	15	4.1k
Lihua Zhang China	8	2.4k 1.1×	1.5k 1.0×	852 1.0×	707 1.1×	614 1.0×	12	4.2k
Raúl Ramos United States	15	2.5k 1.1×	1.7k 1.1×	898 1.1×	692 1.1×	640 1.1×	32	5.1k
Ilya Korsunsky United States	12	3.1k 1.4×	1.7k 1.1×	747 0.9×	749 1.2×	434 0.7×	19	4.9k
Yuriy Baglaenko United States	12	3.0k 1.3×	1.8k 1.2×	747 0.9×	701 1.1×	418 0.7×	24	4.9k
Nghia Millard United States	4	2.8k 1.2×	1.4k 1.0×	690 0.8×	710 1.1×	413 0.7×	7	4.3k
Suoqin Jin China	17	3.0k 1.3×	1.8k 1.2×	1.0k 1.2×	769 1.2×	696 1.1×	34	5.4k
Jasper Wouters Belgium	23	3.3k 1.4×	1.4k 0.9×	1.1k 1.2×	845 1.3×	379 0.6×	44	4.9k
Carmen Bravo González‐Blas Belgium	14	3.4k 1.5×	1.2k 0.8×	671 0.8×	783 1.2×	309 0.5×	16	4.7k
Mallar Bhattacharya United States	15	1.5k 0.7×	1.1k 0.8×	553 0.7×	542 0.8×	716 1.2×	24	3.1k
Peggy Myung United States	23	3.6k 1.6×	2.4k 1.6×	1.2k 1.4×	785 1.2×	687 1.1×	40	6.9k

Countries citing papers authored by Ivan Chang

Since Specialization

Citations

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

Fields of papers citing papers by Ivan Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ivan Chang

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Hielscher, Emily, et al.. (2023). Sense of purpose interventions for depression and anxiety in youth: A scoping review and cross-cultural youth consultation. Journal of Affective Disorders. 339. 887–932. 2 indexed citations

Jin, Suoqin, Christian F. Guerrero‐Juarez, Lihua Zhang, et al.. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications. 12(1). 1088–1088. 3825 indexed citations breakdown →

Thakar, Juilee, Qian Yu, Xing Qiu, et al.. (2020). Unbiased analysis of peripheral blood mononuclear cells reveals CD4 T cell response to RSV matrix protein. Vaccine X. 5. 100065–100065. 2 indexed citations

Patterson, Kurt, Chr̀istophe Magnan, Ivan Chang, et al.. (2019). Local features determine Ty3 targeting frequency at RNA polymerase III transcription start sites. Genome Research. 29(8). 1298–1309. 8 indexed citations

Yu, Qian, Ivan Chang, Richard H. Scheuermann, et al.. (2019). Machine Learning of Discriminative Gate Locations for Clinical Diagnosis. Cytometry Part A. 97(3). 296–307. 5 indexed citations

Patterson, Kurt, et al.. (2018). Functional genomics for the oleaginous yeast Yarrowia lipolytica. Metabolic Engineering. 48. 184–196. 32 indexed citations

Lee, Alexandra, Ivan Chang, Julie G. Burel, et al.. (2018). DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytometry Part A. 93(6). 597–610. 13 indexed citations

Bakken, Trygve E., Lindsay G. Cowell, Brian D. Aevermann, et al.. (2017). Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics. 18(S17). 559–559. 27 indexed citations

Magnan, Chr̀istophe, James Yu, Ivan Chang, et al.. (2016). Sequence Assembly of Yarrowia lipolytica Strain W29/CLIB89 Shows Transposable Element Diversity. PLoS ONE. 11(9). e0162363–e0162363. 67 indexed citations

10.

Bilanchone, Virginia, Robyn M. Kaake, Anthony R. Dawson, et al.. (2015). Ty3 Retrotransposon Hijacks Mating Yeast RNA Processing Bodies to Infect New Genomes. PLoS Genetics. 11(9). e1005528–e1005528. 19 indexed citations

11.

Wang, Charlotte, et al.. (2014). Challenges of Statistical and Machine Learning on Supervised Learning with Class-imbalanced Data.. 52(1). 59–84. 1 indexed citations

12.

Chang, Ivan & Pierre Baldi. (2013). A unifying kinetic framework for modeling oxidoreductase-catalyzed reactions. Bioinformatics. 29(10). 1299–1307. 1 indexed citations

13.

Chang, Ivan, Margit Heiske, Thierry Letellier, Douglas C. Wallace, & Pierre Baldi. (2011). Modeling of Mitochondria Bioenergetics Using a Composable Chemiosmotic Energy Transduction Rate Law: Theory and Experimental Validation. PLoS ONE. 6(9). e14820–e14820. 7 indexed citations

14.

Pierron, Denis, Ivan Chang, Amal Arachiche, et al.. (2011). Mutation Rate Switch inside Eurasian Mitochondrial Haplogroups: Impact of Selection and Consequences for Dating Settlement in Europe. PLoS ONE. 6(6). e21543–e21543. 19 indexed citations

15.

Chang, Ivan, et al.. (2003). A three-dimensional, stochastic simulation of biofilm growth and transport-related factors that affect structure. Microbiology. 149(10). 2859–2871. 50 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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