Matthias Schlesner

55.1k total citations · 2 hit papers
94 papers, 9.7k citations indexed

About

Matthias Schlesner is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Matthias Schlesner has authored 94 papers receiving a total of 9.7k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Molecular Biology, 23 papers in Genetics and 22 papers in Cancer Research. Recurrent topics in Matthias Schlesner's work include Cancer Genomics and Diagnostics (16 papers), Genetic factors in colorectal cancer (11 papers) and Bioinformatics and Genomic Networks (10 papers). Matthias Schlesner is often cited by papers focused on Cancer Genomics and Diagnostics (16 papers), Genetic factors in colorectal cancer (11 papers) and Bioinformatics and Genomic Networks (10 papers). Matthias Schlesner collaborates with scholars based in Germany, United States and Czechia. Matthias Schlesner's co-authors include Roland Eils, Zuguang Gu, Benedikt Brors, Lei Gu, Naveed Ishaque, Bjoern Titz, Peter Uetz, Nagarajan Paramasivam, Reiner Siebert and Stefan Wiemann and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Matthias Schlesner

87 papers receiving 9.7k citations

Hit Papers

Complex heatmaps reveal patterns and correlations in mult... 2014 2026 2018 2022 2016 2014 1000 2.0k 3.0k 4.0k 5.0k

Peers

Matthias Schlesner
Lang Zhou China
Zuguang Gu Germany
Li Zhan China
Zehan Dai China
Cheng Li China
Lang Zhou China
Matthias Schlesner
Citations per year, relative to Matthias Schlesner Matthias Schlesner (= 1×) peers Lang Zhou

Countries citing papers authored by Matthias Schlesner

Since Specialization
Citations

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

Fields of papers citing papers by Matthias Schlesner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthias Schlesner

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Schlesner. A scholar is included among the top collaborators of Matthias Schlesner 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 Matthias Schlesner. Matthias Schlesner 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.
Betz, Andreas, Zuguang Gu, Matthias Schlesner, et al.. (2025). Reconstitution of interferon regulatory factor 7 expression restores interferon beta induction in Huh7 cells. Journal of Virology. 99(6). e0070325–e0070325.
2.
Burghaus, Ina, Tobias Keßler, Felix Sahm, et al.. (2024). PerSurge (NOA-30) phase II trial of perampanel treatment around surgery in patients with progressive glioblastoma. BMC Cancer. 24(1). 135–135. 26 indexed citations
3.
Kretzmer, Helene, et al.. (2024). Navigating Illumina DNA methylation data: biology versus technical artefacts. NAR Genomics and Bioinformatics. 6(4). lqae181–lqae181. 1 indexed citations
4.
Nguyen, Duy, Sara Najafi, Norman Mack, et al.. (2024). Synergy of retinoic acid and BH3 mimetics in MYC(N)-driven embryonal nervous system tumours. British Journal of Cancer. 131(4). 763–777. 1 indexed citations
5.
Chun, Hye-Jung E., Karolina Nemes, Marlena Mucha, et al.. (2024). Clinical and Molecular Risk Factors in Extracranial Malignant Rhabdoid Tumors: Toward an Integrated Model of High-Risk Tumors. Clinical Cancer Research. 30(20). 4667–4680.
6.
Jayne, Sandrine, Cristina López, Inga Nagel, et al.. (2024). The chromosomal translocation t (1;6)(p35.3;p25.2), recurrent in chronic lymphocytic leukaemia, leads to RCC1 :: IRF4 fusion. British Journal of Haematology. 205(6). 2321–2326.
7.
Thiebes, Scott, et al.. (2023). Explainable artificial intelligence for omics data: a systematic mapping study. Briefings in Bioinformatics. 25(1). 30 indexed citations
8.
Miao, Beiping, Dagmara Dymerska, Nagarajan Paramasivam, et al.. (2022). Whole-Exome Sequencing Identifies a Novel Germline Variant in PTK7 Gene in Familial Colorectal Cancer. International Journal of Molecular Sciences. 23(3). 1295–1295. 7 indexed citations
9.
Berker, Yannick, Heike Peterziel, Petra Fiesel, et al.. (2022). Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids. IEEE Transactions on Medical Imaging. 41(12). 3981–3999. 5 indexed citations
10.
Park, Jeongbin, Sebastian Tiesmeyer, Brian Long, et al.. (2021). Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data. Nature Communications. 12(1). 4103–4103. 1 indexed citations
11.
Park, Jeongbin, Sebastian Tiesmeyer, Brian Long, et al.. (2021). Cell segmentation-free inference of cell types from in situ transcriptomics data. Nature Communications. 12(1). 3545–3545. 75 indexed citations
12.
Miao, Beiping, Abhishek Kumar, Dagmara Dymerska, et al.. (2021). A Novel Low-Risk Germline Variant in the SH2 Domain of the SRC Gene Affects Multiple Pathways in Familial Colorectal Cancer. Journal of Personalized Medicine. 11(4). 262–262.
13.
Gengenbacher, Nicolas, Mahak Singhal, Carolin Mogler, et al.. (2020). Timed Ang2-Targeted Therapy Identifies the Angiopoietin–Tie Pathway as Key Regulator of Fatal Lymphogenous Metastasis. Cancer Discovery. 11(2). 424–445. 21 indexed citations
14.
Quintero, Andrés, Daniel Hübschmann, Nils Kurzawa, et al.. (2020). ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets. Biology Methods and Protocols. 5(1). bpaa022–bpaa022. 9 indexed citations
15.
Whalley, Justin P., Ivo Buchhalter, Esther Rheinbay, et al.. (2020). Framework for quality assessment of whole genome cancer sequences. Nature Communications. 11(1). 5040–5040. 4 indexed citations
16.
Mazur, Johanna, et al.. (2019). Genetic Interactions and Tissue Specificity Modulate the Association of Mutations with Drug Response. Molecular Cancer Therapeutics. 19(3). 927–936. 6 indexed citations
17.
Dietz, Steffen, Petros Christopoulos, Lisa Gu, et al.. (2019). Serial liquid biopsies for detection of treatment failure and profiling of resistance mechanisms in KLC1–ALK-rearranged lung cancer. Molecular Case Studies. 5(6). a004630–a004630. 17 indexed citations
18.
Tirier, Stephan M., Jeongbin Park, Friedrich Preußer, et al.. (2019). Pheno-seq – linking visual features and gene expression in 3D cell culture systems. Scientific Reports. 9(1). 14 indexed citations
19.
Agaimy, Abbas, Matthias Bieg, Michael Michal, et al.. (2016). Recurrent Somatic PDGFRB Mutations in Sporadic Infantile/Solitary Adult Myofibromas But Not in Angioleiomyomas and Myopericytomas. The American Journal of Surgical Pathology. 41(2). 195–203. 62 indexed citations
20.
Schlesner, Matthias & Roland Eils. (2015). Hypermutation takes the driver’s seat. Genome Medicine. 7(1). 31–31. 14 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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