Haiyuan Yu
- Molecular Biology top 0.5%
- Computational Theory and Mathematics top 0.2%
- Genetics top 2%
- Cell Biology top 2%
- Plant Science top 5%
- Co-authors
- Mark GersteinAlberto PaccanaroTamás NepuszM SnyderJishnu DasNicholas M. LuscombeValery TrifonovPhilip M. Kim
- Topics
- Bioinformatics and Genomic Networks (58 papers)Microbial Metabolic Engineering and Bioproduction (16 papers)Fungal and yeast genetics research (16 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Haiyuan Yu
125 papers receiving 9.8k citations
Hit Papers
Peers
Comparison fields: 5 of 182
- Molecular Biology 8.2k
- Computational Theory and Mathematics 1.4k
- Genetics 963
- Cell Biology 628
- Plant Science 574
Countries citing papers authored by Haiyuan Yu
This map shows the geographic impact of Haiyuan 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 Haiyuan Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haiyuan Yu more than expected).
Fields of papers citing papers by Haiyuan Yu
This network shows the impact of papers produced by Haiyuan 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 Haiyuan Yu. The network helps show where Haiyuan Yu may publish in the future.
Co-authorship network of co-authors of Haiyuan Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Haiyuan Yu. A scholar is included among the top collaborators of Haiyuan Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Haiyuan Yu. Haiyuan Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 8 | |
| 3 | 3 | |
| 4 | 21 | |
| 5 | 1 | |
| 6 | 10 | |
| 7 | 4 | |
| 8 | 3 | |
| 9 | 9 | |
| 10 | 26 | |
| 11 | 45 | |
| 12 | 4 | |
| 13 | 11 | |
| 14 | 74 | |
| 15 | 22 | |
| 16 | Detecting overlapping protein complexes in protein-protein interaction networksbreakdown → | 919 |
| 17 | 59 | |
| 18 | 291 | |
| 19 | The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamicsbreakdown → | 737 |
| 20 | A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Databreakdown → | 916 |
About Haiyuan Yu
Haiyuan Yu is a scholar working on Molecular Biology, Aging and Spectroscopy, having authored 129 papers that have together received 9.9k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (58 papers), Microbial Metabolic Engineering and Bioproduction (16 papers) and Fungal and yeast genetics research (16 papers). The work is most often cited by research in Molecular Biology (8.2k citations), Aging (163 citations) and Computational Theory and Mathematics (1.4k citations). Haiyuan Yu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Mark Gerstein, Alberto Paccanaro, Tamás Nepusz, M Snyder, Jishnu Das, Nicholas M. Luscombe, Valery Trifonov, Philip M. Kim, Dov Greenbaum and Emmett Sprecher. Their work appears in journals such as Nature, Science and Cell.
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.