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About the database ScholarIQanswers from OpenAlex & ORCID
How has Mengge Yang's publication output changed over time?
ScholarIQpublication output · 2016–2024
Output grew0% over the shown period — from 1 works in 2016 to 1 in 2024.
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1
1
1
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4
1
2016201920202021202220232024
What are the most-cited papers on Mengge Yang?
ScholarIQmost cited works
The Mutations and Clinical Variability in Maternally Inherited Diabetes and Deafness: An Analysis of 161 Patients
Mengge Yang, Lusi Xu, Chunmei Xu, Yuying Cui, Shan Jiang, Jianjun Dong, Lin Liao
Frontiers in Endocrinology. 202153 CitationsOPEN ACCESS
CAMSAP1 breaks the homeostatic microtubule network to instruct neuronal polarity
Zhengrong Zhou, Honglin Xu, Yuejia Li, Mengge Yang, Rui Zhang, Aki Shiraishi, Hiroshi Kiyonari, Xin Liang, Xiahe Huang, Yingchun Wang, Qi Xie, Shuai Liu, Rongqing Chen, Lan Bao, Weixiang Guo, Yu Wang, Wenxiang Meng
Proceedings of the National Academy of Sciences. 202039 CitationsOPEN ACCESS
Biological characteristics of transcription factor RelB in different immune cell types: implications for the treatment of multiple sclerosis
Mengge Yang, Li Sun, Jinming Han, Chao Zheng, Hudong Liang, Jie Zhu, Tao Jin
S182046277. 201930 CitationsOPEN ACCESS
Real-time field disease identification based on a lightweight model
Siyu Quan, Jiajia Wang, Zhenhong Jia, Qiqi Xu, Mengge Yang
Computers and Electronics in Agriculture. 202427 Citations
MS-Net: a novel lightweight and precise model for plant disease identification
Siyu Quan, Jiajia Wang, Zhenhong Jia, Mengge Yang, Qiqi Xu
Frontiers in Plant Science. 202325 CitationsOPEN ACCESS
Related on ScholarIQ
Beijing Chao-Yang Hospital, Capital Medical University
Institution
The Mutations and Clinical Variability in Maternally Inherited Diabetes and Deafness: An Analysis of 161 Patients
Paper
CAMSAP1 breaks the homeostatic microtubule network to instruct neuronal polarity
Paper
Biological characteristics of transcription factor RelB in different immune cell types: implications for the treatment of multiple sclerosis
Paper
Real-time field disease identification based on a lightweight model
Paper
MS-Net: a novel lightweight and precise model for plant disease identification
Paper