Ute Haas

1.4k citations
36 papers · 1.1k · h-index 19

Impact in

  • Hepatology top 2%
    • Liver physiology and pathology
    • Liver Disease and Transplantation
  • Epidemiology top 10%
    • Liver Disease Diagnosis and Treatment

Papers in

    • Liver Disease Diagnosis and Treatment 14
    • Connective Tissue Growth Factor Research 6
    • TGF-β signaling in diseases 3

Ute Haas

32 papers receiving 1.1k citations

Peers

Ute Haas
Comparison fields: 5 of 94
  • Hepatology 310
  • Epidemiology 380
  • Cancer Research 132
  • Complementary and alternative medicine 72
  • Pharmacology 63
Replace Shi Yue with:
Shi Yue China
Chunqing Zhang China
Tomomi Toyonaga Japan
Akihisa Miyazaki Japan
Shoichi Kageyama Japan
Silvia Taffetani Italy
L. Trozzi Italy
Lucia Russo United States
G Ramadori Germany
Ute Haas relative to Shi Yue China Shi Yue's profile →
Citations per field
00.5×
Shi Yue · 1×
Citations per year

Countries citing papers authored by Ute Haas

Since Specialization
Citations

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

Fields of papers citing papers by Ute Haas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012119
2 2013114
3 2009113
4 201482
5 201368
6 201866
7 201750
8 201246
9 201546
10 201742
11 201540
12 201337
13 201034
14 201531
15 201331
16 201528
17 200623
18 201222
19 201918
20 201816

About Ute Haas

Ute Haas is a scholar working on Epidemiology, Molecular Biology, Hepatology, Pathology and Forensic Medicine and Oncology, having authored 36 papers that have together received 1.1k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (14 papers), Liver physiology and pathology (11 papers), Connective Tissue Growth Factor Research (6 papers), Alcohol Consumption and Health Effects (4 papers), Endoplasmic Reticulum Stress and Disease (4 papers), Cancer-related Molecular Pathways (4 papers), TGF-β signaling in diseases (3 papers) and Drug-Induced Hepatotoxicity and Protection (3 papers). The work is most often cited by research in Hepatology (310 citations), Epidemiology (380 citations), Cancer Research (132 citations), Complementary and alternative medicine (72 citations) and Pharmacology (63 citations). Ute Haas has collaborated with scholars based in Germany, United States and Spain. Frequent co-authors include Ralf Weiskirchen, Erawan Borkham‐Kamphorst, Lidia Tihaa, Eddy Van de Leur, Christian Liedtke, Yulia A. Nevzorova, Frank Tacke, Christian Trautwein, Wei Hu and Francisco Javier Cubero. Their work appears in journals such as Cellular Signalling, Journal of Hepatology, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Hepatology and International Journal of Molecular Sciences.

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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