T Maass

976 total citations
25 papers, 720 citations indexed

About

T Maass is a scholar working on Molecular Biology, Hepatology and Epidemiology. According to data from OpenAlex, T Maass has authored 25 papers receiving a total of 720 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 7 papers in Hepatology and 6 papers in Epidemiology. Recurrent topics in T Maass's work include Liver Disease Diagnosis and Treatment (6 papers), Bioinformatics and Genomic Networks (6 papers) and Gene expression and cancer classification (6 papers). T Maass is often cited by papers focused on Liver Disease Diagnosis and Treatment (6 papers), Bioinformatics and Genomic Networks (6 papers) and Gene expression and cancer classification (6 papers). T Maass collaborates with scholars based in Germany, United States and Switzerland. T Maass's co-authors include Andreas Teufel, Peter R. Galle, M Krupp, Frank Staib, Timo Itzel, Stephan Kanzler, F Thieringer, Henning Schulze‐Bergkamen, Binje Vick and Marcus Schuchmann and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Gastroenterology.

In The Last Decade

T Maass

24 papers receiving 712 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T Maass Germany	15	388	229	176	133	112	25	720
Kunimaro Furuta Japan	17	293 0.8×	405 1.8×	298 1.7×	131 1.0×	55 0.5×	32	776
Shi Zuo China	14	484 1.2×	182 0.8×	174 1.0×	221 1.7×	142 1.3×	52	837
Quentin Bayard France	9	302 0.8×	162 0.7×	185 1.1×	245 1.8×	105 0.9×	13	630
Renay L. Bauer United States	11	269 0.7×	230 1.0×	318 1.8×	99 0.7×	93 0.8×	12	652
Yukio Kume Japan	12	447 1.2×	196 0.9×	203 1.2×	58 0.4×	75 0.7×	18	737
Toshimasa Okada Japan	12	344 0.9×	164 0.7×	171 1.0×	175 1.3×	135 1.2×	44	658
Mio Endo Japan	11	310 0.8×	160 0.7×	63 0.4×	131 1.0×	81 0.7×	23	526
Wenbing Sun China	14	226 0.6×	126 0.6×	240 1.4×	189 1.4×	121 1.1×	25	602
Bijun Qiu China	12	445 1.1×	139 0.6×	98 0.6×	197 1.5×	185 1.7×	29	691
Saadia Faouzi France	9	350 0.9×	174 0.8×	386 2.2×	120 0.9×	238 2.1×	12	879

Countries citing papers authored by T Maass

Since Specialization

Citations

This map shows the geographic impact of T Maass'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 T Maass with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T Maass more than expected).

Fields of papers citing papers by T Maass

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T Maass

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

All Works

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20 of 20 papers shown

Itzel, Timo, Rainer Spang, T Maass, et al.. (2019). Random gene sets in predicting survival of patients with hepatocellular carcinoma. Journal of Molecular Medicine. 97(6). 879–888. 14 indexed citations

Teufel, Andreas, Timo Itzel, Wiebke Erhart, et al.. (2016). Comparison of Gene Expression Patterns Between Mouse Models of Nonalcoholic Fatty Liver Disease and Liver Tissues From Patients. Gastroenterology. 151(3). 513–525.e0. 153 indexed citations

Maass, T, Jens U. Marquardt, Ju‐Seog Lee, et al.. (2015). Increased liver carcinogenesis and enrichment of stem cell properties in livers of Dickkopf 2 (Dkk2) deleted mice. Oncotarget. 7(20). 28903–28913. 7 indexed citations

Staib, Frank, M Krupp, T Maass, et al.. (2013). CellMinerHCC: a microarray‐based expression database for hepatocellular carcinoma cell lines. Liver International. 34(4). 621–631. 14 indexed citations

Teufel, Andreas, Diana Becker, Susanne N. Weber, et al.. (2012). Identification of RARRES1 as a core regulator in liver fibrosis. Journal of Molecular Medicine. 90(12). 1439–1447. 9 indexed citations

Marquardt, Jens U., T Maass, M Krupp, et al.. (2012). 385 MOLECULAR STAGES OF PDGFB DRIVEN LIVER FIBROSIS: LESONS FROM A TRANSGENIC MOUSE MODEL. Journal of Hepatology. 56. S155–S155.

Krupp, M, Timo Itzel, T Maass, et al.. (2012). CellLineNavigator: a workbench for cancer cell line analysis. Nucleic Acids Research. 41(D1). D942–D948. 14 indexed citations

Becker, Diana, M Krupp, Frank Staib, et al.. (2012). Genetic signatures shared in embryonic liver development and liver cancer define prognostically relevant subgroups in HCC. Molecular Cancer. 11(1). 55–55. 30 indexed citations

Thieringer, F, T Maass, Erik Meyer, et al.. (2011). Liver‐specific overexpression of matrix metalloproteinase 9 (MMP‐9) in transgenic mice accelerates development of hepatocellular carcinoma. Molecular Carcinogenesis. 51(6). 439–448. 23 indexed citations

10.

Krupp, M, T Maass, Jens U. Marquardt, et al.. (2011). The functional cancer map: A systems-level synopsis of genetic deregulation in cancer. BMC Medical Genomics. 4(1). 53–53. 28 indexed citations

11.

Staib, Frank, Arndt Weinmann, M Krupp, et al.. (2011). 253 CELLMINER HCC: A MICROARRAY BASED EXPRESSION DATABASE FOR HEPATOCELLULAR CARCINOMA CELL LINES. Journal of Hepatology. 54. S104–S104. 1 indexed citations

12.

Maass, T, et al.. (2010). Microarray-Based Gene Expression Analysis of Hepatocellular Carcinoma. Current Genomics. 11(4). 261–268. 29 indexed citations

13.

Maass, T, F Thieringer, Amrit Mann, et al.. (2010). Liver specific overexpression of platelet‐derived growth factor‐B accelerates liver cancer development in chemically induced liver carcinogenesis. International Journal of Cancer. 128(6). 1259–1268. 60 indexed citations

14.

Teufel, Andreas, T Maass, Susanne Strand, et al.. (2010). Liver-specific Ldb1 deletion results in enhanced liver cancer development. Journal of Hepatology. 53(6). 1078–1084. 15 indexed citations

15.

Buchkremer, S., M Krupp, Arndt Weinmann, et al.. (2010). Library of molecular associations: curating the complex molecular basis of liver diseases. BMC Genomics. 11(1). 189–189. 12 indexed citations

16.

Wang, Chunxia, T Maass, M Krupp, et al.. (2009). A systems biology perspective on cholangiocellular carcinoma development: Focus on MAPK-signaling and the extracellular environment. Journal of Hepatology. 50(6). 1122–1131. 18 indexed citations

17.

Vick, Binje, Achim Weber, Toni Urbanik, et al.. (2008). Knockout of myeloid cell leukemia-1 induces liver damage and increases apoptosis susceptibility of murine hepatocytes #. Hepatology. 49(2). 627–636. 125 indexed citations

18.

Thieringer, F, T Maass, Borut Klopcic, et al.. (2008). Spontaneous hepatic fibrosis in transgenic mice overexpressing PDGF-A. Gene. 423(1). 23–28. 42 indexed citations

19.

Weinmann, Arndt, et al.. (2008). BlotBase: A northern blot database. Gene. 427(1-2). 47–50. 11 indexed citations

20.

Klopcic, Borut, T Maass, Erik Meyer, et al.. (2007). TGF-β superfamily signaling is essential for tooth and hair morphogenesis and differentiation. European Journal of Cell Biology. 86(11-12). 781–799. 41 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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