Nancy Yu
Impact in
- Cancer Research top 5%
- MicroRNA in disease regulation
- Endocrinology top 2%
Papers in
-
- Breast Cancer Treatment Studies 5
- MicroRNA in disease regulation 4
-
- Lymphoma Diagnosis and Treatment 3
- Co-authors
- Fiona S. L. Brinkman (4 shared papers)Matthew R. Laird (2 shared papers)Leonard J. Foster (1 shared paper)Cenk Sahinalp (1 shared paper)Phuong Dao (1 shared paper)Martin Ester (1 shared paper)Gabor Melli (1 shared paper)Raymond Lo (1 shared paper)
- Journals
- Blood (3 papers)Nature Communications (3 papers)Nucleic Acids Research (3 papers)Scientific Reports (2 papers)Science Signaling (2 papers)
- Partner nations
- SwedenUnited StatesCanada
In The Last Decade
Nancy Yu
27 papers receiving 4.1k citations
Nancy Yu's Hit Papers
Peers
Comparison fields: 5 of 111
- Cancer Research 495
- Endocrinology 151
- Microbiology 173
- Molecular Biology 1.5k
- Genetics 592
Countries citing papers authored by Nancy Yu
This map shows the geographic impact of Nancy 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 Nancy Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nancy Yu more than expected).
Fields of papers citing papers by Nancy Yu
This network shows the impact of papers produced by Nancy 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 Nancy Yu. The network helps show where Nancy Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Nancy 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
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes Hit paper breakdown → | 2010 | 2054 |
| 2 | 2010 | 528 | |
| 3 | 2011 | 263 | |
| 4 | 2009 | 237 | |
| 5 | 2017 | 192 | |
| 6 | 2011 | 128 | |
| 7 | 2013 | 122 | |
| 8 | 2015 | 91 | |
| 9 | 2010 | 76 | |
| 10 | 2018 | 75 | |
| 11 | 2017 | 59 | |
| 12 | 2005 | 54 | |
| 13 | 2019 | 42 | |
| 14 | 2011 | 34 | |
| 15 | 2015 | 33 | |
| 16 | 2017 | 18 | |
| 17 | 2017 | 18 | |
| 18 | 2020 | 18 | |
| 19 | 2021 | 18 | |
| 20 | 2020 | 16 |
About Nancy Yu
Nancy Yu is a scholar working on Cancer Research, Pathology and Forensic Medicine, Genetics, Molecular Biology and Genetics, having authored 27 papers that have together received 4.1k indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (5 papers), MicroRNA in disease regulation (4 papers), Lymphoma Diagnosis and Treatment (3 papers), Machine Learning in Bioinformatics (3 papers), Chronic Lymphocytic Leukemia Research (2 papers), Prostate Cancer Treatment and Research (2 papers), Genomics and Phylogenetic Studies (2 papers) and Reading and Literacy Development (1 paper). The work is most often cited by research in Cancer Research (495 citations), Endocrinology (151 citations), Microbiology (173 citations), Molecular Biology (1.5k citations) and Genetics (592 citations). Nancy Yu has collaborated with scholars based in Sweden, United States and Canada. Frequent co-authors include Fiona S. L. Brinkman, Matthew R. Laird, Leonard J. Foster, Cenk Sahinalp, Phuong Dao, Martin Ester, Gabor Melli, Raymond Lo, Robert E. W. Hancock and Geoffrey L. Winsor. Their work appears in journals such as Blood, Nature Communications, Nucleic Acids Research, Scientific Reports and Science Signaling.
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.