Jingkai Yu
Impact in
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- Bioinformatics and Genomic Networks
- Microbial Metabolic Engineering and Bioproduction
- Protein Structure and Dynamics
- Genomics and Phylogenetic Studies
- Gene expression and cancer classification
- Gene Regulatory Network Analysis
- Machine Learning in Bioinformatics
Papers in
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- Bioinformatics and Genomic Networks 12
- Machine Learning in Bioinformatics 3
- Biomedical Text Mining and Ontologies 3
- Protein Structure and Dynamics 3
- Signaling Pathways in Disease 2
- Gene expression and cancer classification 2
- Co-authors
- Russell L. Finley (8 shared papers)Svetlana Pacifico (3 shared papers)Guozhen Liu (2 shared papers)Tao Hu (2 shared papers)Trey Ideker (2 shared papers)Farshad Fotouhi (3 shared papers)Thilakam Murali (2 shared papers)Stephen T. Guest (2 shared papers)
- Journals
- BMC Systems Biology (2 papers)PLoS ONE (2 papers)Nature Methods (1 paper)Scientific Reports (1 paper)BMC Genomics (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Jingkai Yu
15 papers receiving 745 citations
Peers
Comparison fields: 5 of 100
- Aging 18
- Molecular Biology 573
- Computational Theory and Mathematics 89
- Cell Biology 61
- Cellular and Molecular Neuroscience 55
Countries citing papers authored by Jingkai Yu
This map shows the geographic impact of Jingkai Yu'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 Jingkai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingkai Yu more than expected).
Fields of papers citing papers by Jingkai Yu
This network shows the impact of papers produced by Jingkai Yu. 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 Jingkai Yu. The network helps show where Jingkai Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Jingkai Yu, 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 | 2007 | 175 | |
| 2 | 2010 | 150 | |
| 3 | 2008 | 93 | |
| 4 | 2013 | 75 | |
| 5 | 2008 | 71 | |
| 6 | 2008 | 37 | |
| 7 | 2015 | 36 | |
| 8 | 2006 | 28 | |
| 9 | 2013 | 27 | |
| 10 | 2015 | 18 | |
| 11 | 2016 | 14 | |
| 12 | 2013 | 10 | |
| 13 | 2011 | 9 | |
| 14 | 2011 | 8 | |
| 15 | 2016 | 5 | |
| 16 | 2005 | 1 |
About Jingkai Yu
Jingkai Yu is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Oncology and Spectroscopy, having authored 16 papers that have together received 757 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (12 papers), Machine Learning in Bioinformatics (3 papers), Computational Drug Discovery Methods (3 papers), Biomedical Text Mining and Ontologies (3 papers), Protein Structure and Dynamics (3 papers), Peptidase Inhibition and Analysis (2 papers), Signaling Pathways in Disease (2 papers) and Gene expression and cancer classification (2 papers). The work is most often cited by research in Aging (18 citations), Molecular Biology (573 citations), Computational Theory and Mathematics (89 citations), Cell Biology (61 citations) and Cellular and Molecular Neuroscience (55 citations). Jingkai Yu has collaborated with scholars based in China and United States. Frequent co-authors include Russell L. Finley, Svetlana Pacifico, Guozhen Liu, Tao Hu, Trey Ideker, Farshad Fotouhi, Thilakam Murali, Stephen T. Guest, George G. Roberts and Ariel Schwartz. Their work appears in journals such as BMC Systems Biology, PLoS ONE, Nature Methods, Scientific Reports and BMC Genomics.
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.