Ming Gang Lin
- Molecular Biology top 10%
- Genetics top 5%
- Cancer Research top 5%
- Oncology top 10%
- Pulmonary and Respiratory Medicine
- Co-authors
- Cheng LiWilliam R. SellersWing Hung WongLee‐Jen WeiMatthew MeyersonPeggy L. PorterXiaopu YuanRameen Beroukhim
- Topics
- Breast Cancer Treatment Studies (5 papers)Cancer Genomics and Diagnostics (4 papers)Gene expression and cancer classification (4 papers)
- Cited by
- Cancer ResearchGeneticsOncology
- Partner nations
- United StatesChinaNorway
In The Last Decade
Ming Gang Lin
17 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 79
- Molecular Biology 777
- Genetics 561
- Cancer Research 516
- Oncology 410
- Pulmonary and Respiratory Medicine 168
Countries citing papers authored by Ming Gang Lin
This map shows the geographic impact of Ming Gang Lin'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 Ming Gang Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Gang Lin more than expected).
Fields of papers citing papers by Ming Gang Lin
This network shows the impact of papers produced by Ming Gang Lin. 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 Ming Gang Lin. The network helps show where Ming Gang Lin may publish in the future.
Co-authorship network of co-authors of Ming Gang Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Ming Gang Lin. A scholar is included among the top collaborators of Ming Gang Lin 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 Ming Gang Lin. Ming Gang Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 55 | |
| 3 | 25 | |
| 4 | 16 | |
| 5 | 29 | |
| 6 | 119 | |
| 7 | 54 | |
| 8 | 242 | |
| 9 | 37 | |
| 10 | [Randomized trial of breast self-examination in 266,064 women in Shanghai]. | 8 |
| 11 | 64 | |
| 12 | 25 | |
| 13 | 111 | |
| 14 | 292 | |
| 15 | 59 | |
| 16 | 150 | |
| 17 | Genome-wide loss of heterozygosity analysis from laser capture microdissected prostate cancer using single nucleotide polymorphic allele (SNP) arrays and a novel bioinformatics platform dChipSNP. | 95 |
About Ming Gang Lin
Ming Gang Lin is a scholar working on Cancer Research, Genetics and Pathology and Forensic Medicine, having authored 17 papers that have together received 1.4k indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (5 papers), Cancer Genomics and Diagnostics (4 papers) and Gene expression and cancer classification (4 papers). The work is most often cited by research in Cancer Research (516 citations), Genetics (561 citations) and Oncology (410 citations). Ming Gang Lin has collaborated with scholars based in United States, China and Norway. Frequent co-authors include Cheng Li, William R. Sellers, Wing Hung Wong, Lee‐Jen Wei, Matthew Meyerson, Peggy L. Porter, Xiaopu Yuan, Rameen Beroukhim, Levi A. Garraway and David M. Tanenbaum. Their work appears in journals such as Bioinformatics, JNCI Journal of the National Cancer Institute and Cancer.
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.