Junha Cha
Impact in
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- Single-cell and spatial transcriptomics
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- RNA modifications and cancer
Papers in
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- Single-cell and spatial transcriptomics 8
- Bioinformatics and Genomic Networks 7
- Gene expression and cancer classification 3
- Gene Regulatory Network Analysis 3
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- Cell Image Analysis Techniques 1
- Co-authors
- Insuk Lee (10 shared papers)Seungbyn Baek (5 shared papers)Chan Yeong Kim (1 shared paper)Traver Hart (1 shared paper)Eiru Kim (1 shared paper)Sunmo Yang (1 shared paper)Edward M. Marcotte (1 shared paper)Jae-Won Cho (2 shared papers)
- Journals
- Nucleic Acids Research (2 papers)PLoS Biology (1 paper)Clinical Cancer Research (1 paper)Computational and Structural Biotechnology Journal (1 paper)Animal Cells and Systems (1 paper)
- Partner nations
- South KoreaUnited StatesIsrael
In The Last Decade
Junha Cha
11 papers receiving 240 citations
Peers
Comparison fields: 5 of 53
- Biophysics 14
- Molecular Biology 162
- Otorhinolaryngology 9
- Cancer Research 28
- Immunology 22
Countries citing papers authored by Junha Cha
This map shows the geographic impact of Junha Cha'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 Junha Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junha Cha more than expected).
Fields of papers citing papers by Junha Cha
This network shows the impact of papers produced by Junha Cha. 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 Junha Cha. The network helps show where Junha Cha may publish in the future.
Co-authors
The 25 scholars most cited alongside Junha Cha, 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 | 2020 | 92 | |
| 2 | 2021 | 86 | |
| 3 | 2024 | 17 | |
| 4 | 2024 | 17 | |
| 5 | 2022 | 11 | |
| 6 | 2025 | 8 | |
| 7 | 2023 | 4 | |
| 8 | 2025 | 3 | |
| 9 | 2025 | 2 | |
| 10 | 2025 | 2 | |
| 11 | 2025 | 1 | |
| 12 | 2025 | 0 |
About Junha Cha
Junha Cha is a scholar working on Molecular Biology, Biophysics, Oncology, Computational Theory and Mathematics and Infectious Diseases, having authored 12 papers that have together received 243 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (8 papers), Bioinformatics and Genomic Networks (7 papers), Gene expression and cancer classification (3 papers), Gene Regulatory Network Analysis (3 papers), Cancer Immunotherapy and Biomarkers (2 papers), Cell Image Analysis Techniques (1 paper), Computational Drug Discovery Methods (1 paper) and Mycobacterium research and diagnosis (1 paper). The work is most often cited by research in Biophysics (14 citations), Molecular Biology (162 citations), Otorhinolaryngology (9 citations), Cancer Research (28 citations) and Immunology (22 citations). Junha Cha has collaborated with scholars based in South Korea, United States and Israel. Frequent co-authors include Insuk Lee, Seungbyn Baek, Chan Yeong Kim, Traver Hart, Eiru Kim, Sunmo Yang, Edward M. Marcotte, Jae-Won Cho, Martin Hemberg and Sun Och Yoon. Their work appears in journals such as Nucleic Acids Research, PLoS Biology, Clinical Cancer Research, Computational and Structural Biotechnology Journal and Animal Cells and Systems.
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.