Michael Baudis

6.0k total citations
78 papers, 2.4k citations indexed

About

Michael Baudis is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Michael Baudis has authored 78 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Molecular Biology, 40 papers in Cancer Research and 38 papers in Genetics. Recurrent topics in Michael Baudis's work include Cancer Genomics and Diagnostics (38 papers), Genomic variations and chromosomal abnormalities (30 papers) and Gene expression and cancer classification (14 papers). Michael Baudis is often cited by papers focused on Cancer Genomics and Diagnostics (38 papers), Genomic variations and chromosomal abnormalities (30 papers) and Gene expression and cancer classification (14 papers). Michael Baudis collaborates with scholars based in Switzerland, Germany and United States. Michael Baudis's co-authors include Peter Lichter, Michael L. Cleary, Ruthild G. Weber, Reiner Siebert, Guido Reifenberger, V. Peter Collins, Marietta Wolter, Jan Boström, Evert-Jan G. Boerma and P. M. Kluin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Clinical Oncology.

In The Last Decade

Michael Baudis

72 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Baudis Switzerland 25 1.2k 636 622 599 581 78 2.4k
Jeffrey Swensen United States 28 1.2k 1.0× 754 1.2× 747 1.2× 1.3k 2.2× 755 1.3× 87 3.1k
Nicolas Sévenet France 20 2.3k 2.0× 329 0.5× 1.1k 1.8× 575 1.0× 271 0.5× 51 3.1k
Shashikant Kulkarni United States 22 1.1k 1.0× 930 1.5× 538 0.9× 674 1.1× 760 1.3× 51 3.0k
Huimin Geng United States 31 1.9k 1.6× 372 0.6× 821 1.3× 768 1.3× 178 0.3× 115 3.3k
Marc Zapatka Germany 30 1.5k 1.3× 674 1.1× 217 0.3× 921 1.5× 264 0.5× 80 2.6k
Socorro Marıá Rodríguez-Pinilla Spain 30 1.6k 1.4× 1.1k 1.7× 1.1k 1.8× 2.0k 3.3× 274 0.5× 97 3.8k
Michel Longy France 35 2.4k 2.1× 864 1.4× 1.0k 1.6× 1.3k 2.2× 940 1.6× 93 4.0k
Diana Mandelker United States 23 1.3k 1.1× 718 1.1× 388 0.6× 867 1.4× 311 0.5× 94 2.5k
Subhadra V. Nandula United States 16 1.4k 1.2× 782 1.2× 885 1.4× 894 1.5× 174 0.3× 30 2.7k
Rose‐Marie Amini Sweden 26 612 0.5× 549 0.9× 1.1k 1.8× 1.2k 2.0× 206 0.4× 98 2.4k

Countries citing papers authored by Michael Baudis

Since Specialization
Citations

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

Fields of papers citing papers by Michael Baudis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Baudis

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

All Works

20 of 20 papers shown
1.
Sonay, Tugce Bilgin, et al.. (2025). Multicancer analyses of short tandem repeat variations reveal shared gene regulatory mechanisms. Briefings in Bioinformatics. 26(3).
2.
Baudis, Michael, et al.. (2024). cancercelllines.org—a novel resource for genomic variants in cancer cell lines. Database. 2024. 1 indexed citations
3.
4.
Fromont, Lauren A., Mauricio Moldes, Michael Baudis, et al.. (2024). Twelve quick tips for deploying a Beacon. PLoS Computational Biology. 20(3). e1011817–e1011817.
5.
Baudis, Michael, et al.. (2023). labelSeg: segment annotation for tumor copy number alteration profiles. Briefings in Bioinformatics. 25(2). 1 indexed citations
6.
Rambla, Jordi, Michael Baudis, Tim Beck, et al.. (2022). Beacon v2 and Beacon networks: A “lingua franca” for federated data discovery in biomedical genomics, and beyond. Human Mutation. 43(6). 791–799. 22 indexed citations
7.
Huang, Qingyao, et al.. (2021). The Progenetix oncogenomic resource in 2021. Database. 2021. 14 indexed citations
8.
Thorogood, Adrian, Heidi L. Rehm, Peter Goodhand, et al.. (2021). International federation of genomic medicine databases using GA4GH standards. Cell Genomics. 1(2). 100032–100032. 22 indexed citations
9.
Huang, Qingyao & Michael Baudis. (2020). Enabling population assignment from cancer genomes with SNP2pop. Scientific Reports. 10(1). 4846–4846. 6 indexed citations
10.
Putora, Paul Martin, Michael Baudis, Beth M. Beadle, et al.. (2020). Oncology Informatics: Status Quo and Outlook. Oncology. 98(6). 329–331. 7 indexed citations
11.
Gao, Bo & Michael Baudis. (2020). Minimum error calibration and normalization for genomic copy number analysis. Genomics. 112(5). 3331–3341. 4 indexed citations
12.
Huang, Qingyao, et al.. (2019). DNA copy number imbalances in primary cutaneous lymphomas. Journal of the European Academy of Dermatology and Venereology. 33(6). 1062–1075. 8 indexed citations
13.
Valtorta, Emanuele, Franco Armelao, Roberto Togni, et al.. (2012). Improved multiplex ligation‐dependent probe amplification analysis identifies a deleterious PMS2 allele generated by recombination with crossover between PMS2 and PMS2CL. Genes Chromosomes and Cancer. 51(9). 819–831. 16 indexed citations
14.
Bug, Stefanie, Jan Dürig, Florian Oyen, et al.. (2009). Recurrent loss, but lack of mutations, of the SMARCB1 tumor suppressor gene in T-cell prolymphocytic leukemia with TCL1A–TCRAD juxtaposition. Cancer Genetics and Cytogenetics. 192(1). 44–47. 14 indexed citations
15.
Boerma, Evert-Jan G., Reiner Siebert, P. M. Kluin, & Michael Baudis. (2008). Translocations involving 8q24 in Burkitt lymphoma and other malignant lymphomas: a historical review of cytogenetics in the light of todays knowledge. Leukemia. 23(2). 225–234. 137 indexed citations
16.
Mao, Xin, et al.. (2005). Genetic losses in breast cancer: toward an integrated molecular cytogenetic map. Cancer Genetics and Cytogenetics. 160(2). 141–151. 12 indexed citations
17.
Baudis, Michael, et al.. (2005). ABCB1 over‐expression and drug‐efflux in acute lymphoblastic leukemia cell lines with t(17;19) and E2A‐HLF expression. Pediatric Blood & Cancer. 47(6). 757–764. 15 indexed citations
18.
Hidalgo‐Miranda, Alfredo, Michael Baudis, Iver Petersen, et al.. (2005). Microarray comparative genomic hybridization detection of chromosomal imbalances in uterine cervix carcinoma. BMC Cancer. 5(1). 77–77. 69 indexed citations
19.
Weßendorf, Swen, Peter Lichter, Carsten Schwänen, et al.. (2001). Potential of chromosomal and matrix-based comparative genomic hybridization for molecular diagnostics in lymphomas. Annals of Hematology. 80(S3). B35–B37. 1 indexed citations
20.
Bentz, Martin, Ulf S.R. Bergerheim, Chunde Li, et al.. (1996). Chromosome imbalances in papillary renal cell carcinoma and first cytogenetic data of familial cases analyzed by comparative genomic hybridization. Cytogenetic and Genome Research. 75(1). 17–21. 44 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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