Walden Ai

2.3k citations
27 papers · 1.9k · h-index 22

Impact in

    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research
  • Immunology top 5%
    • Immune cells in cancer

Papers in

    • Kruppel-like factors research 16
    • Cancer-related gene regulation 10
    • Epigenetics and DNA Methylation 3
    • Immune cells in cancer 5
    • Immunotherapy and Immune Responses 2

Walden Ai

26 papers receiving 1.9k citations

Peers

Walden Ai
Comparison fields: 5 of 89
  • Cancer Research 348
  • Immunology 419
  • Molecular Biology 1.2k
  • Oncology 385
  • Hematology 139
Replace Qing Rao with:
Qing Rao China
Muxiang Zhou United States
Mario I. Vega United States
Sathish Kumar Mungamuri India
Wei Du United States
Daniela S. Daniela Sanchez Bassères Brazil
Mattia Frontini United Kingdom
James S. Hardwick United States
Rachel A. O’Keefe United States
Briana J. Williams United States
Walden Ai relative to Qing Rao China Qing Rao's profile →
Citations per field
00.5×1.5×2.0×
Qing Rao · 1×
Citations per year

Countries citing papers authored by Walden Ai

Since Specialization
Citations

This map shows the geographic impact of Walden Ai'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 Walden Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Walden Ai more than expected).

Fields of papers citing papers by Walden Ai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Walden Ai. 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 Walden Ai. The network helps show where Walden Ai may publish in the future.

Co-authors

The 25 scholars most cited alongside Walden Ai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Walden Ai Line = papers co-authored together Walden Ai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011374
2 2008329
3 2010184
4 2020110
5 200896
6 201495
7 201861
8 201260
9 201252
10 201251
11 201646
12 201545
13 201344
14 201942
15 201340
16 200739
17 201336
18 201435
19 202032
20 200929

About Walden Ai

Walden Ai is a scholar working on Molecular Biology, Immunology, Genetics, Oncology and Public Health, Environmental and Occupational Health, having authored 27 papers that have together received 1.9k indexed citations. Recurring topics across this work include Kruppel-like factors research (16 papers), Cancer-related gene regulation (10 papers), Immune cells in cancer (5 papers), Genetic Syndromes and Imprinting (5 papers), Epigenetics and DNA Methylation (3 papers), Cancer Cells and Metastasis (2 papers), Acute Lymphoblastic Leukemia research (2 papers) and Immunotherapy and Immune Responses (2 papers). The work is most often cited by research in Cancer Research (348 citations), Immunology (419 citations), Molecular Biology (1.2k citations), Oncology (385 citations) and Hematology (139 citations). Walden Ai has collaborated with scholars based in United States, China and Netherlands. Frequent co-authors include Shiang Huang, Swapan K. Ray, Hexin Chen, Daping Fan, Jie Fu, Heng Zheng, Jing Li, Timothy C. Wang, Hai Zheng and Fang Yu. Their work appears in journals such as OncoImmunology, PLoS ONE, Nature Medicine, International Journal of Cancer and American Journal of Physiology-Gastrointestinal and Liver Physiology.

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.

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