Lin Wan
Impact in
- Cancer Research top 5%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Biophysics top 5%
Papers in ⓘ
-
- Single-cell and spatial transcriptomics 19
- Gene expression and cancer classification 11
- Genomics and Phylogenetic Studies 10
- Gene Regulatory Network Analysis 9
- RNA Research and Splicing 6
- RNA modifications and cancer 6
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- Cancer-related molecular mechanisms research 7
- Co-authors
- Kai Cao (4 shared papers)Yiguang Hong (3 shared papers)Da Pang (6 shared papers)Shouping Xu (6 shared papers)Xiangqi Bai (4 shared papers)Fengzhu Sun (4 shared papers)Gesine Reinert (2 shared papers)Michael S. Waterman (2 shared papers)
- Journals
- Bioinformatics (8 papers)Theranostics (2 papers)Cellular Physiology and Biochemistry (2 papers)Briefings in Bioinformatics (2 papers)BMC Bioinformatics (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Lin Wan
69 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 131
- Cancer Research 467
- Biophysics 90
- Molecular Biology 1.0k
- Cognitive Neuroscience 88
- Genetics 93
Countries citing papers authored by Lin Wan
This map shows the geographic impact of Lin Wan'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 Lin Wan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lin Wan more than expected).
Fields of papers citing papers by Lin Wan
This network shows the impact of papers produced by Lin Wan. 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 Lin Wan. The network helps show where Lin Wan may publish in the future.
Co-authors
The 25 scholars most cited alongside Lin Wan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 71 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 127 | |
| 2 | 2019 | 121 | |
| 3 | 2017 | 94 | |
| 4 | 2020 | 78 | |
| 5 | 2010 | 76 | |
| 6 | 2009 | 72 | |
| 7 | 2020 | 68 | |
| 8 | 2021 | 55 | |
| 9 | 2016 | 52 | |
| 10 | 2022 | 45 | |
| 11 | 2018 | 41 | |
| 12 | 2017 | 41 | |
| 13 | 2010 | 37 | |
| 14 | 2017 | 33 | |
| 15 | 2011 | 29 | |
| 16 | 2017 | 26 | |
| 17 | 2016 | 26 | |
| 18 | 2018 | 24 | |
| 19 | 2006 | 21 | |
| 20 | 2020 | 20 |
About Lin Wan
Lin Wan is a scholar working on Molecular Biology, Cancer Research, Genetics, Biophysics and Oncology, having authored 71 papers that have together received 1.4k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (19 papers), Gene expression and cancer classification (11 papers), Genomics and Phylogenetic Studies (10 papers), Gene Regulatory Network Analysis (9 papers), Cell Image Analysis Techniques (8 papers), Cancer-related molecular mechanisms research (7 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (6 papers). The work is most often cited by research in Cancer Research (467 citations), Biophysics (90 citations), Molecular Biology (1.0k citations), Cognitive Neuroscience (88 citations) and Genetics (93 citations). Lin Wan has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Kai Cao, Yiguang Hong, Da Pang, Shouping Xu, Xiangqi Bai, Fengzhu Sun, Gesine Reinert, Michael S. Waterman, Peiyuan Wang and Hao Wu. Their work appears in journals such as Bioinformatics, Theranostics, Cellular Physiology and Biochemistry, Briefings in Bioinformatics and BMC Bioinformatics.
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.