Kai Huang

16.9k citations
395 papers · 11.0k indexed · 3 hit papers · h-index 54

Impact in

    • Cancer-related molecular mechanisms research
    • MicroRNA in disease regulation
    • Circular RNAs in diseases
    • RNA modifications and cancer
    • Mitochondrial Function and Pathology

Papers in

    • Cancer-related molecular mechanisms research 16
    • MicroRNA in disease regulation 15
    • PARP inhibition in cancer therapy 18
    • Peptidase Inhibition and Analysis 15

Kai Huang

366 papers receiving 10.9k citations

Hit Papers

Field-assisted machining of difficult-to-machine materials 2024 · 67 citations
67202220262023202450100150200

Peers

Kai Huang
Comparison fields: 5 of 200
  • Cancer Research 2.5k
  • Molecular Biology 5.2k
  • Nephrology 444
  • Oncology 1.3k
  • Neurology 655
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Man Li China
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Kai Huang relative to Man Li China Man Li's profile →
Citations per field
00.5×1.5×2.4×
Man Li · 1×
Citations per year

Countries citing papers authored by Kai Huang

Since Specialization
Citations

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

Fields of papers citing papers by Kai Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Kai Huang, 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 Kai Huang Line = papers co-authored together Kai Huang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202511
2 20252
3 20251
4 20250
5 20250
6 202411
7 20247
8 202415
9 20240
10 202410
11 202310
12 202331
13 20237
14 20237
15 20232
16 20234
17 202365
18 201879
19 201385
20 2012134

About Kai Huang

Kai Huang is a scholar working on Cancer Research, Oncology, Molecular Biology, Cardiology and Cardiovascular Medicine and Cell Biology, having authored 395 papers that have together received 11.0k indexed citations. Recurring topics across this work include Signaling Pathways in Disease (19 papers), PARP inhibition in cancer therapy (18 papers), Cancer-related molecular mechanisms research (16 papers), MicroRNA in disease regulation (15 papers), Peptidase Inhibition and Analysis (15 papers), Advanced Battery Materials and Technologies (15 papers), Advancements in Battery Materials (15 papers) and Adipose Tissue and Metabolism (15 papers). The work is most often cited by research in Cancer Research (2.5k citations), Molecular Biology (5.2k citations), Nephrology (444 citations), Oncology (1.3k citations) and Neurology (655 citations). Kai Huang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Qiangsong Tong, Liduan Zheng, Dan Huang, Hong Mei, Huajie Song, Fengxiao Zhang, Yang Feng, Minglu Liang, Xiaoxiang Mao and Yajun Chen. Their work appears in journals such as Cell Death and Disease, Frontiers in Pharmacology, Diabetes, Biochemical and Biophysical Research Communications and PLoS ONE.

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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