Huzhang Mao

2.3k citations
14 papers · 1.1k indexed · 1 hit paper · h-index 9
Topics
Statistical Methods and Bayesian Inference (4 papers)Advanced Causal Inference Techniques (3 papers)Diabetes Treatment and Management (3 papers)
Partner nations
United StatesJapanItaly

In The Last Decade

Huzhang Mao

14 papers receiving 1.1k citations

Hit Papers

Efficacy and safety of a novel dual GIP and GLP-1 recepto...20212026202220242021200400600

Peers

Huzhang Mao
Comparison fields: 5 of 108
  • Endocrinology, Diabetes and Metabolism 572
  • Molecular Biology 427
  • Pharmacology 250
  • Surgery 210
  • Physiology 191
Replace Vivian T. Thieu with:
Vivian T. Thieu United States
Atalanta Ghosh United States
Donghui Kan United States
Tao Gao United States
Christian M. Shaffer United States
David Mackintosh United Kingdom
Charlotte Brasch‐Andersen Denmark
Marlena Maziarz United States
Jennifer Dong United States
Tatsuo Yanagawa Japan
Huzhang Mao relative to Vivian T. Thieu United States Vivian T. Thieu's profile →
Citations per field
00.5×1.5×
Vivian T. Thieu · 1×
Citations per year

Countries citing papers authored by Huzhang Mao

Since Specialization
Citations

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

Fields of papers citing papers by Huzhang Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Huzhang Mao

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

All Works

14 of 14 papers shown
#WorkIndexed citations
1 29
2 4
3
Efficacy and safety of a novel dual GIP and GLP-1 receptor agonist tirzepatide in patients with type 2 diabetes (SURPASS-1): a double-blind, randomised, phase 3 trialbreakdown →
648
4 2
5 31
6 11
7 5
8 12
9 8
10 34
11 79
12 57
13 226
14 1

About Huzhang Mao

Huzhang Mao is a scholar working on Statistics and Probability, Endocrinology, Diabetes and Metabolism and Statistics, Probability and Uncertainty, having authored 14 papers that have together received 1.1k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (4 papers), Advanced Causal Inference Techniques (3 papers) and Diabetes Treatment and Management (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (572 citations), Pharmacology (250 citations) and Statistics and Probability (86 citations). Huzhang Mao has collaborated with scholars based in United States, Japan and Italy. Frequent co-authors include Laura Fernández Landó, Clare J. Lee, Vivian T. Thieu, Shizuka Kaneko, Chrisanthi A. Karanikas, Xuewei Cui, Juan P. Frías, Julio Rosenstock, Carol Wysham and Liang Li. Their work appears in journals such as The Lancet, Nucleic Acids Research and Circulation.

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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