Kunal Jindal
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Single-cell and spatial transcriptomics
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- CRISPR and Genetic Engineering
- Pluripotent Stem Cells Research
Papers in
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- Gene Regulatory Network Analysis 3
- Single-cell and spatial transcriptomics 3
- Pluripotent Stem Cells Research 1
- CRISPR and Genetic Engineering 1
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- Adipokines, Inflammation, and Metabolic Diseases 1
- Co-authors
- Samantha A. Morris (4 shared papers)Kenji Kamimoto (4 shared papers)Christy M. Hoffmann (2 shared papers)Blerta Stringa (1 shared paper)Lilianna Solnica‐Krezel (1 shared paper)Xue Yang (2 shared papers)Guillermo C. Rivera-Gonzalez (2 shared papers)Naoto Yamaguchi (1 shared paper)
- Journals
- Nature Biotechnology (1 paper)Nature (1 paper)Cell stem cell (1 paper)Stem Cell Reports (1 paper)
- Partner nations
- United States
In The Last Decade
Kunal Jindal
3 papers receiving 316 citations
Kunal Jindal's Hit Papers
Peers
Comparison fields: 5 of 49
- Biophysics 30
- Molecular Biology 257
- Cancer Research 35
- Developmental Neuroscience 7
- Immunology 26
Countries citing papers authored by Kunal Jindal
This map shows the geographic impact of Kunal Jindal'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 Kunal Jindal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kunal Jindal more than expected).
Fields of papers citing papers by Kunal Jindal
This network shows the impact of papers produced by Kunal Jindal. 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 Kunal Jindal. The network helps show where Kunal Jindal may publish in the future.
Co-authors
The 12 scholars most cited alongside Kunal Jindal, 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 | Dissecting cell identity via network inference and in silico gene perturbation Hit paper breakdown → | 2023 | 254 |
| 2 | 2023 | 41 | |
| 3 | 2022 | 22 | |
| 4 | 2025 | 0 |
About Kunal Jindal
Kunal Jindal is a scholar working on Molecular Biology, Epidemiology, Cell Biology, Physiology and Cancer Research, having authored 4 papers that have together received 317 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (3 papers), Single-cell and spatial transcriptomics (3 papers), Zebrafish Biomedical Research Applications (1 paper), Adipokines, Inflammation, and Metabolic Diseases (1 paper), Cancer Genomics and Diagnostics (1 paper), Pluripotent Stem Cells Research (1 paper), CRISPR and Genetic Engineering (1 paper) and Adipose Tissue and Metabolism (1 paper). The work is most often cited by research in Biophysics (30 citations), Molecular Biology (257 citations), Cancer Research (35 citations), Developmental Neuroscience (7 citations) and Immunology (26 citations). Kunal Jindal has collaborated with scholars based in United States. Frequent co-authors include Samantha A. Morris, Kenji Kamimoto, Christy M. Hoffmann, Blerta Stringa, Lilianna Solnica‐Krezel, Xue Yang, Guillermo C. Rivera-Gonzalez, Naoto Yamaguchi, Wenjun Kong and Rachel L. Mintz. Their work appears in journals such as Nature Biotechnology, Nature, Cell stem cell and Stem Cell Reports.
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.