Ivan Chang
Impact in
- Immunology top 2%
- Immune cells in cancer
- Immune Cell Function and Interaction
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
Papers in
-
- Single-cell and spatial transcriptomics 2
- RNA and protein synthesis mechanisms 2
- Microbial Metabolic Engineering and Bioproduction 2
- Gene expression and cancer classification 1
- Fungal and yeast genetics research 1
- Co-authors
- Lihua Zhang (1 shared paper)Chen‐Hsiang Kuan (1 shared paper)Qing Nie (1 shared paper)Maksim V. Plikus (1 shared paper)Christian F. Guerrero‐Juarez (1 shared paper)Peggy Myung (1 shared paper)Raúl Ramos (1 shared paper)Suoqin Jin (1 shared paper)
- Journals
- PLoS ONE (3 papers)Cytometry Part A (2 papers)Vaccine X (1 paper)Genome Research (1 paper)Microbiology (1 paper)
- Partner nations
- United StatesFranceEstonia
In The Last Decade
Ivan Chang
15 papers receiving 4.5k citations
Ivan Chang's Hit Papers
Peers
Comparison fields: 5 of 147
- Immunology 1.1k
- Cancer Research 459
- Neurology 233
- Molecular Biology 2.0k
- Oncology 585
Countries citing papers authored by Ivan Chang
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
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-authors
The 25 scholars most cited alongside Ivan Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Inference and analysis of cell-cell communication using CellChat Hit paper breakdown → | 2021 | 4213 |
| 2 | 2016 | 70 | |
| 3 | 2003 | 50 | |
| 4 | 2018 | 33 | |
| 5 | 2017 | 27 | |
| 6 | 2011 | 19 | |
| 7 | 2015 | 19 | |
| 8 | 2018 | 13 | |
| 9 | 2019 | 8 | |
| 10 | 2011 | 7 | |
| 11 | 2019 | 5 | |
| 12 | 2023 | 4 | |
| 13 | 2020 | 2 | |
| 14 | 2013 | 1 | |
| 15 | Challenges of Statistical and Machine Learning on Supervised Learning with Class-imbalanced Data. | 2014 | 1 |
About Ivan Chang
Ivan Chang is a scholar working on Molecular Biology, Artificial Intelligence, Plant Science, Biomedical Engineering and Computer Networks and Communications, having authored 15 papers that have together received 4.5k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (2 papers), RNA and protein synthesis mechanisms (2 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), Marine Biology and Environmental Chemistry (1 paper), Forensic and Genetic Research (1 paper), Gene expression and cancer classification (1 paper), Fungal and yeast genetics research (1 paper) and Plant Disease Resistance and Genetics (1 paper). The work is most often cited by research in Immunology (1.1k citations), Cancer Research (459 citations), Neurology (233 citations), Molecular Biology (2.0k citations) and Oncology (585 citations). Ivan Chang has collaborated with scholars based in United States, France and Estonia. Frequent co-authors include Lihua Zhang, Chen‐Hsiang Kuan, Qing Nie, Maksim V. Plikus, Christian F. Guerrero‐Juarez, Peggy Myung, Raúl Ramos, Suoqin Jin, Suzanne Sandmeyer and Jay D. Keasling. Their work appears in journals such as PLoS ONE, Cytometry Part A, Vaccine X, Genome Research and Microbiology.
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.