Guangchuang Yu
- Cancer Research top 0.05%
- Molecular Biology top 0.05%
- Bioinformatics and Genomic Networks 20
- Genomics and Phylogenetic Studies 16
- Gene expression and cancer classification 12
- Metabolomics and Mass Spectrometry Studies 7
- Gut microbiota and health 7
- Machine Learning in Bioinformatics 4
- Single-cell and spatial transcriptomics 4
- Immunology top 0.2%
- Pulmonary and Respiratory Medicine top 0.1%
- Aging top 0.5%
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- Species Distribution and Climate Change 4
- Journals
- Bioinformatics (5 papers)The Innovation (5 papers)Molecular Biology and Evolution (3 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Guangchuang Yu
77 papers receiving 41.6k citations
Hit Papers
Peers
Comparison fields: 5 of 201
- Cancer Research 8.1k
- Molecular Biology 23.6k
- Immunology 6.5k
- Pulmonary and Respiratory Medicine 6.6k
- Aging 342
Countries citing papers authored by Guangchuang Yu
This map shows the geographic impact of Guangchuang 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 Guangchuang Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangchuang Yu more than expected).
Fields of papers citing papers by Guangchuang Yu
This network shows the impact of papers produced by Guangchuang 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 Guangchuang Yu. The network helps show where Guangchuang Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guangchuang 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 4 | |
| 5 | 2025 | 1 | |
| 6 | 2025 | 3 | |
| 7 | 2024 | 6 | |
| 8 | 2024 | 36 | |
| 9 | 2024 | 7 | |
| 10 | 2024 | 38 | |
| 11 | 2023 | 4 | |
| 12 | ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Databreakdown → | 2021 | 177 |
| 13 | clusterProfiler 4.0: A universal enrichment tool for interpreting omics databreakdown → | 2021 | 7083 |
| 14 | 2018 | 1 | |
| 15 | 2016 | 2926 | |
| 16 | 2016 | 32 | |
| 17 | ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualizationbreakdown → | 2015 | 2372 |
| 18 | clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clustersbreakdown → | 2012 | 21362 |
| 19 | 2012 | 101 | |
| 20 | 2010 | 11 |
About Guangchuang Yu
Guangchuang Yu is a scholar working on Ecological Modeling, Molecular Biology and Paleontology, having authored 82 papers that have together received 41.8k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (20 papers), Genomics and Phylogenetic Studies (16 papers), Gene expression and cancer classification (12 papers), Metabolomics and Mass Spectrometry Studies (7 papers), Gut microbiota and health (7 papers), Machine Learning in Bioinformatics (4 papers), Single-cell and spatial transcriptomics (4 papers) and Species Distribution and Climate Change (4 papers). The work is most often cited by research in Cancer Research (8.1k citations), Molecular Biology (23.6k citations) and Immunology (6.5k citations). Guangchuang Yu has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Qing‐Yu He, Yanyan Han, Yi Guan, Tommy Tsan‐Yuk Lam, Huachen Zhu, Shuangbin Xu, Li Zhan, Meijun Chen, Lang Zhou and Tingze Feng. Their work appears in journals such as Bioinformatics, The Innovation, Molecular Biology and Evolution, PROTEOMICS and Frontiers in Genetics.
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.