Amber W. Wang

1.9k total citations · 1 hit paper
9 papers, 774 citations indexed

About

Amber W. Wang is a scholar working on Molecular Biology, Hepatology and Genetics. According to data from OpenAlex, Amber W. Wang has authored 9 papers receiving a total of 774 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Hepatology and 3 papers in Genetics. Recurrent topics in Amber W. Wang's work include Liver physiology and pathology (5 papers), Genetics and Neurodevelopmental Disorders (2 papers) and Liver Disease Diagnosis and Treatment (2 papers). Amber W. Wang is often cited by papers focused on Liver physiology and pathology (5 papers), Genetics and Neurodevelopmental Disorders (2 papers) and Liver Disease Diagnosis and Treatment (2 papers). Amber W. Wang collaborates with scholars based in United States, France and Israel. Amber W. Wang's co-authors include Matthew B. Buechler, Merone Roose‐Girma, Joseph R. Arron, Shannon J. Turley, Richard Bourgon, Roger Caothien, Lucinda Tam, Akshay T. Krishnamurty, Rachana Pradhan and Yeqing Angela Yang and has published in prestigious journals such as Nature, Journal of Clinical Investigation and Genes & Development.

In The Last Decade

Amber W. Wang

9 papers receiving 769 citations

Hit Papers

Cross-tissue organization of the fibroblast lineage 2021 2026 2022 2024 2021 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Amber W. Wang United States 7 407 190 183 152 85 9 774
Iris Augustin Germany 11 526 1.3× 128 0.7× 89 0.5× 92 0.6× 60 0.7× 16 756
Shihong Lu China 19 440 1.1× 198 1.0× 210 1.1× 183 1.2× 78 0.9× 59 1.1k
Eveline S. M. de Jonge‐Muller Netherlands 10 196 0.5× 158 0.8× 143 0.8× 147 1.0× 62 0.7× 14 649
Bram Piersma Netherlands 7 399 1.0× 264 1.4× 117 0.6× 144 0.9× 145 1.7× 9 1.0k
Mitsugu Tanii Japan 10 411 1.0× 230 1.2× 138 0.8× 258 1.7× 47 0.6× 14 760
Tanya A. Rege United States 8 339 0.8× 153 0.8× 80 0.4× 116 0.8× 148 1.7× 13 735
Haruko Shima Japan 13 505 1.2× 190 1.0× 207 1.1× 86 0.6× 80 0.9× 34 1.2k
Hitoshi Toyoda Japan 10 432 1.1× 359 1.9× 93 0.5× 99 0.7× 83 1.0× 13 812
Gidi Rechavi Israel 17 614 1.5× 265 1.4× 155 0.8× 92 0.6× 52 0.6× 26 1.1k
Rachana Pradhan United States 7 440 1.1× 453 2.4× 329 1.8× 125 0.8× 118 1.4× 7 1.0k

Countries citing papers authored by Amber W. Wang

Since Specialization
Citations

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

Fields of papers citing papers by Amber W. Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amber W. Wang. 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 Amber W. Wang. The network helps show where Amber W. Wang may publish in the future.

Co-authorship network of co-authors of Amber W. Wang

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

All Works

9 of 9 papers shown
1.
Buechler, Matthew B., Rachana Pradhan, Akshay T. Krishnamurty, et al.. (2021). Cross-tissue organization of the fibroblast lineage. Nature. 593(7860). 575–579. 555 indexed citations breakdown →
2.
Reizel, Yitzhak, Ashleigh Morgan, Long Gao, et al.. (2020). Collapse of the hepatic gene regulatory network in the absence of FoxA factors. Genes & Development. 34(15-16). 1039–1050. 35 indexed citations
3.
Zahm, Adam M., Amber W. Wang, Yue J. Wang, et al.. (2019). A High-Content Screen Identifies MicroRNAs That Regulate Liver Repopulation After Injury in Mice. Gastroenterology. 158(4). 1044–1057.e17. 11 indexed citations
4.
Wang, Amber W., Yue J. Wang, Adam M. Zahm, et al.. (2019). The Dynamic Chromatin Architecture of the Regenerating Liver. Cellular and Molecular Gastroenterology and Hepatology. 9(1). 121–143. 40 indexed citations
5.
Wang, Amber W., Adam M. Zahm, & Kirk J. Wangensteen. (2019). Cell Type-specific Gene Expression Profiling in the Mouse Liver. Journal of Visualized Experiments. 1 indexed citations
6.
Wang, Amber W., Adam M. Zahm, & Kirk J. Wangensteen. (2019). Cell Type-specific Gene Expression Profiling in the Mouse Liver. Journal of Visualized Experiments. 1 indexed citations
7.
Wang, Amber W., Kirk J. Wangensteen, Yue J. Wang, et al.. (2018). TRAP-seq identifies cystine/glutamate antiporter as a driver of recovery from liver injury. Journal of Clinical Investigation. 128(6). 2297–2309. 22 indexed citations
8.
Ou, Kristy, Ming Yu, Nicholas Moss, et al.. (2018). Targeted demethylation at the CDKN1C/p57 locus induces human β cell replication. Journal of Clinical Investigation. 129(1). 209–214. 48 indexed citations
9.
Wangensteen, Kirk J., Yue J. Wang, Zhixun Dou, et al.. (2017). Combinatorial genetics in liver repopulation and carcinogenesis with a in vivo CRISPR activation platform†. Hepatology. 68(2). 663–676. 61 indexed citations

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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