Yang Dai
- Cancer Research top 5%
- Reproductive Medicine top 2%
- Immunology top 5%
- Immune Cell Function and Interaction 15
- T-cell and B-cell Immunology 12
- Molecular Biology top 5%
- Gut microbiota and health 19
- Gene expression and cancer classification 16
- Bioinformatics and Genomic Networks 12
- Genomics and Phylogenetic Studies 11
- Obstetrics and Gynecology top 5%
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- Diabetes and associated disorders 13
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- Monoclonal and Polyclonal Antibodies Research 9
- Co-authors
- Zhengdeng LeiPeter E. LarsenDerek ReimanXiaofeng ZhouRuth T. YuRonald M. EvansMichael DownesChristopher Liddle
- Journals
- Journal of Physics B Atomic Molecular and Optical Physics (9 papers)PLoS ONE (9 papers)The Journal of Immunology (7 papers)
- Partner nations
- United StatesJapanChina
In The Last Decade
Yang Dai
152 papers receiving 4.2k citations
Hit Papers
Peers
Comparison fields: 5 of 169
- Cancer Research 602
- Reproductive Medicine 285
- Immunology 703
- Molecular Biology 2.0k
- Obstetrics and Gynecology 197
Countries citing papers authored by Yang Dai
This map shows the geographic impact of Yang Dai'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 Yang Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Dai more than expected).
Fields of papers citing papers by Yang Dai
This network shows the impact of papers produced by Yang Dai. 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 Yang Dai. The network helps show where Yang Dai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yang Dai, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 44 | |
| 7 | 2022 | 6 | |
| 8 | 2021 | 51 | |
| 9 | 2021 | 48 | |
| 10 | 2021 | 14 | |
| 11 | 2020 | 18 | |
| 12 | 2020 | 57 | |
| 13 | 2020 | 53 | |
| 14 | 2019 | 18 | |
| 15 | 2018 | 5 | |
| 16 | 2011 | 55 | |
| 17 | 2010 | 99 | |
| 18 | A novel approach for prediction of protein subcellular localization from sequence using Fourier analysis and support vector machines | 2004 | 2 |
| 19 | Feature Selection of Support Vector Regression for Quantitative Structure-Activity Relationships (QSAR). | 2003 | 0 |
| 20 | 2000 | 11 |
About Yang Dai
Yang Dai is a scholar working on Computer Graphics and Computer-Aided Design, Immunology and Numerical Analysis, having authored 159 papers that have together received 4.3k indexed citations. Recurring topics across this work include Gut microbiota and health (19 papers), Gene expression and cancer classification (16 papers), Immune Cell Function and Interaction (15 papers), Diabetes and associated disorders (13 papers), T-cell and B-cell Immunology (12 papers), Bioinformatics and Genomic Networks (12 papers), Genomics and Phylogenetic Studies (11 papers) and Monoclonal and Polyclonal Antibodies Research (9 papers). The work is most often cited by research in Cancer Research (602 citations), Reproductive Medicine (285 citations) and Immunology (703 citations). Yang Dai has collaborated with scholars based in United States, Japan and China. Frequent co-authors include Zhengdeng Lei, Peter E. Larsen, Derek Reiman, Xiaofeng Zhou, Ruth T. Yu, Ronald M. Evans, Michael Downes, Christopher Liddle, Ahmed A. Metwally and M. Jubayer Rahman. Their work appears in journals such as Journal of Physics B Atomic Molecular and Optical Physics, PLoS ONE, The Journal of Immunology, BMC Bioinformatics and JCI Insight.
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.