M. Schwab

3.2k total citations · 2 hit papers
36 papers, 2.7k citations indexed

About

M. Schwab is a scholar working on Neurology, Molecular Biology and Oncology. According to data from OpenAlex, M. Schwab has authored 36 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Neurology, 20 papers in Molecular Biology and 9 papers in Oncology. Recurrent topics in M. Schwab's work include Neuroblastoma Research and Treatments (23 papers), Cancer therapeutics and mechanisms (8 papers) and Virus-based gene therapy research (6 papers). M. Schwab is often cited by papers focused on Neuroblastoma Research and Treatments (23 papers), Cancer therapeutics and mechanisms (8 papers) and Virus-based gene therapy research (6 papers). M. Schwab collaborates with scholars based in Germany, United Kingdom and United States. M. Schwab's co-authors include J. Michael Bishop, Harold Varmus, Kari Alitalo, Ching‐Shwun Lin, Lawrence W. Stanton, Lukas C. Amler, Werner Rosenau, Maike Busch, Jay W. Ellison and Larissa Savelyeva and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The EMBO Journal and Molecular and Cellular Biology.

In The Last Decade

M. Schwab

36 papers receiving 2.6k citations

Hit Papers

Homogeneously staining chromosomal regions contain amplif... 1983 2026 1997 2011 1983 1984 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Schwab Germany 22 1.8k 1.2k 628 605 520 36 2.7k
Gloria Balaban United States 19 1.0k 0.6× 398 0.3× 630 1.0× 332 0.5× 449 0.9× 23 1.7k
Naotoshi Kanda Japan 19 1.0k 0.6× 678 0.6× 296 0.5× 380 0.6× 306 0.6× 40 1.7k
Virginie Raynal France 26 1.1k 0.6× 581 0.5× 562 0.9× 327 0.5× 688 1.3× 40 2.3k
A. Lee Burns United States 30 1.4k 0.8× 1.1k 0.9× 1.2k 2.0× 396 0.7× 210 0.4× 55 3.5k
Larissa Savelyeva Germany 23 1.0k 0.6× 382 0.3× 273 0.4× 461 0.8× 357 0.7× 49 1.5k
Richard N. Freiman United States 24 1.2k 0.7× 259 0.2× 303 0.5× 483 0.8× 252 0.5× 34 2.1k
A. Berns Netherlands 14 1.1k 0.6× 282 0.2× 453 0.7× 303 0.5× 171 0.3× 18 1.9k
Thomas Look United States 19 2.1k 1.2× 206 0.2× 882 1.4× 299 0.5× 308 0.6× 36 3.1k
Leah Conroy United States 12 1.5k 0.8× 680 0.6× 291 0.5× 135 0.2× 116 0.2× 13 2.3k
A. Jauch Germany 28 1.7k 0.9× 149 0.1× 512 0.8× 596 1.0× 437 0.8× 50 2.6k

Countries citing papers authored by M. Schwab

Since Specialization
Citations

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

Fields of papers citing papers by M. Schwab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Schwab

This figure shows the co-authorship network connecting the top 25 collaborators of M. Schwab. A scholar is included among the top collaborators of M. Schwab 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 M. Schwab. M. Schwab 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.
Schwab, M. & Ashis Kumer Biswas. (2023). Invertible Neural Networks for Trustworthy AI. 480–485. 1 indexed citations
2.
Dreidax, Daniel, et al.. (2010). MYCN/MYC-mediated drug resistance mechanisms in neuroblastoma. International Journal of Clinical Pharmacology and Therapeutics. 48(7). 489–491. 9 indexed citations
3.
Schwab, M.. (2003). MYCN in neuronal tumours. Cancer Letters. 204(2). 179–187. 108 indexed citations
4.
Perri, Patrizia, et al.. (1999). Fine mapping of distal 1p loci reveals TP73 at D1S468. Cytogenetic and Genome Research. 84(1-2). 111–114. 10 indexed citations
5.
Schwab, M., Maximilian Knoll, D. Jentschura, & E. Hagmüller. (1997). Hormone-inactive neuroendocrine tumors of the pancreas. Der Chirurg. 68(7). 705–705. 2 indexed citations
6.
Berthold, Frank, Kurtuluş Şahin, Barbara Hero, et al.. (1997). The current contribution of molecular factors to risk estimation in neuroblastoma patients. European Journal of Cancer. 33(12). 2092–2097. 41 indexed citations
7.
Amler, Lukas C., Raffaella Corvi, Christian Praml, et al.. (1995). Reciprocal translocation at in a neuroblastoma cell line: Isolation of a YAC clone at the break. European Journal of Cancer. 31(4). 527–530. 8 indexed citations
8.
Wenzel, Achim & M. Schwab. (1995). The mycn/max protein complex in neuroblastoma. Short review. European Journal of Cancer. 31(4). 516–519. 43 indexed citations
9.
Corvi, Raffaella, Larissa Savelyeva, Lukas C. Amler, Rupert Handgretinger, & M. Schwab. (1995). Cytogenetic evolution of mycn and mdm2 amplification in the neuroblastoma ls tumour and its cell line. European Journal of Cancer. 31(4). 520–523. 25 indexed citations
10.
Savelyeva, Larissa, Raffaella Corvi, & M. Schwab. (1994). Translocation involving 1p and 17q is a recurrent genetic alteration of human neuroblastoma cells.. PubMed. 55(2). 334–40. 88 indexed citations
11.
Mor, Orna, Guglielmina Nadia Ranzani, Galit Rotman, et al.. (1993). DNA amplification in human gastric carcinomas. Cancer Genetics and Cytogenetics. 65(2). 111–114. 30 indexed citations
12.
Schwab, M.. (1993). Amplification of N-myc as a prognostic marker for patients with neuroblastoma.. PubMed. 4(1). 13–8. 55 indexed citations
13.
Hiller, Sebastian, et al.. (1991). Localization of regulatory elements controlling human MYCN expression.. PubMed. 6(6). 969–77. 30 indexed citations
14.
Schilbach, Karin, Peter Pollwein, M. Schwab, et al.. (1990). Reduction of N-myc expression by antisense RNA is amplified by interferon: Possible involvement of the 2-5A system. Biochemical and Biophysical Research Communications. 170(3). 1242–1248. 7 indexed citations
15.
Bruchelt, Gernot, Rupert Handgretinger, Karin Schilbach, et al.. (1990). Die Rolle der Interferone beim Neuroblastom - Teil 1: Antiproliferative Effekte. Klinische Pädiatrie. 202(4). 202–205. 1 indexed citations
16.
Small, Michael B., Nissim Hay, M. Schwab, & J. Michael Bishop. (1987). Neoplastic transformation by the human gene N-myc.. Molecular and Cellular Biology. 7(5). 1638–1645. 107 indexed citations
17.
Stanton, Lawrence W., M. Schwab, & J. Michael Bishop. (1986). Nucleotide sequence of the human N-myc gene.. Proceedings of the National Academy of Sciences. 83(6). 1772–1776. 177 indexed citations
18.
Ramsay, Gary, Lawrence W. Stanton, M. Schwab, & J. Michael Bishop. (1986). Human proto-oncogene N-myc encodes nuclear proteins that bind DNA.. Molecular and Cellular Biology. 6(12). 4450–4457. 112 indexed citations
19.
Schwab, M., Jay W. Ellison, Maike Busch, et al.. (1984). Enhanced expression of the human gene N-myc consequent to amplification of DNA may contribute to malignant progression of neuroblastoma.. Proceedings of the National Academy of Sciences. 81(15). 4940–4944. 325 indexed citations breakdown →
20.
Schwab, M.. (1982). How can altered differentiation induced by 12-O-tetradecanoylphorbol-13-acetate be related to tumor promotion?. PubMed. 7. 417–26. 1 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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