Chunbo Miao

1.2k citations
16 papers · 879 indexed · 1 hit paper · h-index 11
Topics
Plant Molecular Biology Research (6 papers)DNA Repair Mechanisms (5 papers)Machine Learning in Bioinformatics (3 papers)

In The Last Decade

Chunbo Miao

16 papers receiving 871 citations

Hit Papers

Mutations in a subfamily of abscisic acid receptor genes ...2018202620202023201850100150200250

Peers

Chunbo Miao
Comparison fields: 5 of 47
  • Plant Science 700
  • Molecular Biology 551
  • Genetics 111
  • Cell Biology 78
  • Ecology, Evolution, Behavior and Systematics 27
Replace Ushio Fujikura with:
Ushio Fujikura Japan
Baoxiang Qin China
Christophe Gaillochet Germany
Shenghao Zou China
Guojian Hu China
Michael Pacher Germany
Lucie Pereira France
Xingchen Kong China
Bojun Ma China
Steven Dreißig Germany
Chunbo Miao relative to Ushio Fujikura Japan Ushio Fujikura's profile →
Citations per field
00.5×2.8×
Ushio Fujikura · 1×
Citations per year

Countries citing papers authored by Chunbo Miao

Since Specialization
Citations

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

Fields of papers citing papers by Chunbo Miao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunbo Miao

This figure shows the co-authorship network connecting the top 25 collaborators of Chunbo Miao. A scholar is included among the top collaborators of Chunbo Miao 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 Chunbo Miao. Chunbo Miao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
#WorkIndexed citations
1 4
2 2
3 4
4 3
5 6
6 32
7 16
8 16
9 141
10 89
11
Mutations in a subfamily of abscisic acid receptor genes promote rice growth and productivitybreakdown →
250
12 39
13 38
14 82
15 113
16 44

About Chunbo Miao

Chunbo Miao is a scholar working on Plant Science, Microbiology and Molecular Biology, having authored 16 papers that have together received 879 indexed citations. Recurring topics across this work include Plant Molecular Biology Research (6 papers), DNA Repair Mechanisms (5 papers) and Machine Learning in Bioinformatics (3 papers). The work is most often cited by research in Plant Science (700 citations), Molecular Biology (551 citations) and Business and International Management (11 citations). Chunbo Miao has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Jian‐Kang Zhu, Kai Hua, Ding Tang, Zhukuan Cheng, Lihong Xiao, Changsong Zou, Yang Zhao, Ray A. Bressan, Mo Wang and Shenkui Liu. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and The Plant Cell.

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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