Jun S. Liu
Impact in
- Statistics and Probability top 0.05%
- Molecular Biology top 0.05%
- Genomics and Chromatin Dynamics
- RNA and protein synthesis mechanisms
- RNA modifications and cancer
- RNA Research and Splicing
- Genomics and Phylogenetic Studies
- Epigenetics and DNA Methylation
- Signaling Pathways in Disease
Papers in
-
- Statistical Methods and Inference 41
- Markov Chains and Monte Carlo Methods 29
-
- Gene expression and cancer classification 50
- Genomics and Chromatin Dynamics 37
- Genomics and Phylogenetic Studies 32
- Bioinformatics and Genomic Networks 29
- RNA and protein synthesis mechanisms 27
- Co-authors
- X. Shirley LiuRong ChenTaiwen LiBo LiStuart L. SchreiberWing Hung WongCharles E. LawrenceIrving L. Weissman
- Journals
- Journal of the American Statistical Association (43 papers)Bioinformatics (19 papers)Proceedings of the National Academy of Sciences (12 papers)Biometrika (10 papers)Genome biology (10 papers)
- Partner nations
- United StatesChinaSingapore
In The Last Decade
Jun S. Liu
458 papers receiving 42.8k citations
Hit Papers
Peers
Comparison fields: 5 of 220
- Statistics and Probability 3.1k
- Molecular Biology 24.1k
- Cancer Research 3.9k
- Genetics 5.2k
- Artificial Intelligence 5.7k
Countries citing papers authored by Jun S. Liu
This map shows the geographic impact of Jun S. Liu'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 Jun S. Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun S. Liu more than expected).
Fields of papers citing papers by Jun S. Liu
This network shows the impact of papers produced by Jun S. Liu. 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 Jun S. Liu. The network helps show where Jun S. Liu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun S. Liu, 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 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 0 | |
| 10 | 2024 | 4 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 3 | |
| 13 | 2020 | 8 | |
| 14 | 2020 | 0 | |
| 15 | 2020 | 95 | |
| 16 | 2018 | 2 | |
| 17 | 2018 | 38 | |
| 18 | Sparse Sliced Inverse Regression for High Dimensional Data | 2016 | 2 |
| 19 | Extracting Sequence Features to Predict\nProtein–DNA Interactions: A Comparative Study | 2009 | 34 |
| 20 | 2006 | 20 |
About Jun S. Liu
Jun S. Liu is a scholar working on Statistics and Probability, Molecular Biology, Artificial Intelligence, Genetics and Water Science and Technology, having authored 483 papers that have together received 44.0k indexed citations. Recurring topics across this work include Gene expression and cancer classification (50 papers), Bayesian Methods and Mixture Models (48 papers), Statistical Methods and Inference (41 papers), Genomics and Chromatin Dynamics (37 papers), Genomics and Phylogenetic Studies (32 papers), Markov Chains and Monte Carlo Methods (29 papers), Bioinformatics and Genomic Networks (29 papers) and RNA and protein synthesis mechanisms (27 papers). The work is most often cited by research in Statistics and Probability (3.1k citations), Molecular Biology (24.1k citations), Cancer Research (3.9k citations), Genetics (5.2k citations) and Artificial Intelligence (5.7k citations). Jun S. Liu has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include X. Shirley Liu, Rong Chen, Taiwen Li, Bo Li, Stuart L. Schreiber, Wing Hung Wong, Rong Chen, Charles E. Lawrence, Irving L. Weissman and Jeff Friedman. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics, Proceedings of the National Academy of Sciences, Biometrika and Genome biology.
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.