Ralph Schlapbach

9.3k total citations · 2 hit papers
98 papers, 4.8k citations indexed

About

Ralph Schlapbach is a scholar working on Molecular Biology, Spectroscopy and Cancer Research. According to data from OpenAlex, Ralph Schlapbach has authored 98 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Molecular Biology, 19 papers in Spectroscopy and 12 papers in Cancer Research. Recurrent topics in Ralph Schlapbach's work include Advanced Proteomics Techniques and Applications (18 papers), Genomics and Phylogenetic Studies (17 papers) and Gene expression and cancer classification (14 papers). Ralph Schlapbach is often cited by papers focused on Advanced Proteomics Techniques and Applications (18 papers), Genomics and Phylogenetic Studies (17 papers) and Gene expression and cancer classification (14 papers). Ralph Schlapbach collaborates with scholars based in Switzerland, Germany and United States. Ralph Schlapbach's co-authors include Bernd Roschitzki, A. Fontana, Hubert Rehrauer, Julia A. Vorholt, Nathanaël Delmotte, Christian von Mering, Samuel Chaffron, Claudia Knief, Gerd Innerebner and Christian Panse and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Ralph Schlapbach

96 papers receiving 4.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Community proteogenomics reveals insights into the physiology of phyllosphere bacteria

2009 631 citations Bernd Roschitzki, Ralph Schlapbach et al. profile →
DMSO induces drastic changes in human cellular processes and epigenetic landscape in vitro

2019 286 citations Ralph Schlapbach et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ralph Schlapbach Switzerland	34	2.5k	747	534	409	390	98	4.8k
Qingsong Lin Singapore	44	2.6k 1.1×	494 0.7×	645 1.2×	406 1.0×	158 0.4×	154	5.2k
Sascha Sauer Germany	44	3.7k 1.5×	516 0.7×	897 1.7×	279 0.7×	315 0.8×	119	6.6k
Ananth Prakash United Kingdom	11	3.2k 1.3×	386 0.5×	495 0.9×	477 1.2×	154 0.4×	18	4.9k
Jingwen Bai China	5	2.7k 1.1×	349 0.5×	472 0.9×	416 1.0×	153 0.4×	6	4.4k
Bernd Roschitzki Switzerland	34	2.1k 0.9×	961 1.3×	295 0.6×	398 1.0×	208 0.5×	76	4.2k
José A. Dianes United Kingdom	8	3.2k 1.3×	494 0.7×	475 0.9×	1000 2.4×	141 0.4×	11	5.0k
Attila Csordás United Kingdom	17	4.0k 1.6×	565 0.8×	543 1.0×	1.2k 2.8×	184 0.5×	24	6.3k
Antonius Koller United States	40	4.3k 1.8×	534 0.7×	459 0.9×	553 1.4×	172 0.4×	77	6.5k
David García‐Seisdedos Spain	10	2.6k 1.1×	297 0.4×	441 0.8×	431 1.1×	162 0.4×	16	4.2k
Marina Gritsenko United States	40	3.1k 1.3×	388 0.5×	421 0.8×	1.5k 3.7×	325 0.8×	98	5.1k

Countries citing papers authored by Ralph Schlapbach

Since Specialization

Citations

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

Fields of papers citing papers by Ralph Schlapbach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ralph Schlapbach

This figure shows the co-authorship network connecting the top 25 collaborators of Ralph Schlapbach. A scholar is included among the top collaborators of Ralph Schlapbach 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 Ralph Schlapbach. Ralph Schlapbach 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

Poveda, Lucy, Witold Wolski, Christian Panse, et al.. (2025). A combination of phenotypic responses and genetic adaptations enables Staphylococcus aureus to withstand inhibitory molecules secreted by Pseudomonas aeruginosa. mSystems. 10(8). e0026225–e0026225.

Wolski, Witold, Jonas Grossmann, Peter Leary, et al.. (2025). prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics. Journal of Proteome Research. 24(2). 955–965. 1 indexed citations

Hausmann, Fabian, Lucas Caldi Gomes, Sonja Hänzelmann, et al.. (2024). A dataset profiling the multiomic landscape of the prefrontal cortex in amyotrophic lateral sclerosis. GigaScience. 13. 1 indexed citations

Wolski, Witold, Paolo Nanni, Jonas Grossmann, et al.. (2023). prolfqua : A Comprehensive R -Package for Proteomics Differential Expression Analysis. Journal of Proteome Research. 22(4). 1092–1104. 33 indexed citations

Struja, Tristan, Endre Laczkó, Witold Wolski, et al.. (2022). Association of proteomic markers with nutritional risk and response to nutritional support: A secondary pilot study of the EFFORT trial using an untargeted proteomics approach. Clinical Nutrition ESPEN. 48. 282–290. 5 indexed citations

Qi, Weihong, Andrea Patrignani, Pascal Schläpfer, et al.. (2022). The haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar reveal novel pan-genome and allele-specific transcriptome features. GigaScience. 11. 30 indexed citations

Sobek, Jens, et al.. (2020). Single-molecule chemistry. Part I: monitoring oxidation of G in oligonucleotides using CY3 fluorescence. Methods and Applications in Fluorescence. 8(3). 35010–35010. 4 indexed citations

Trachsel, Christian, Christian Panse, Tobias Kockmann, et al.. (2018). rawDiag: An R Package Supporting Rational LC–MS Method Optimization for Bottom-up Proteomics. Journal of Proteome Research. 17(8). 2908–2914. 25 indexed citations

Caron, Étienne, Romain Roncagalli, Takeshi Hase, et al.. (2017). Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry. Cell Reports. 18(13). 3219–3226. 27 indexed citations

10.

Ardissone, Silvia, Peter Redder, Giancarlo Russo, et al.. (2016). Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in α-Proteobacteria. PLoS Genetics. 12(12). e1006499–e1006499. 17 indexed citations

11.

Snoek, Basten L., Jonas Grossmann, Rita Volkers, et al.. (2016). Natural Genetic Variation Differentially Affects the Proteome and Transcriptome in Caenorhabditis elegans. Molecular & Cellular Proteomics. 15(5). 1670–1680. 19 indexed citations

12.

Dimitrieva, Slavica, Ralph Schlapbach, & Hubert Rehrauer. (2016). Prognostic value of cross-omics screening for kidney clear cell renal cancer survival. Biology Direct. 11(1). 68–68. 21 indexed citations

13.

Russo, Giancarlo, et al.. (2015). Highly sensitive, non-invasive detection of colorectal cancer mutations using single molecule, third generation sequencing. PubMed. 7. 32–39. 15 indexed citations

14.

Okoniewski, Michał, Janine Meienberg, Andrea Patrignani, et al.. (2013). Precise Breakpoint Localization of Large Genomic Deletions using PacBio and Illumina Next-Generation Sequencers. BioTechniques. 54(2). 98–100. 6 indexed citations

15.

Sobek, Jens, Catharine Aquino, Wilfried Weigel, & Ralph Schlapbach. (2013). Drop drying on surfaces determines chemical reactivity - the specific case of immobilization of oligonucleotides on microarrays. PubMed. 6(1). 8–8. 18 indexed citations

16.

Sedić, Mirela, Peter Gehrig, Mike Scott, et al.. (2008). Differential antiproliferative mechanisms of novel derivative of benzimidazo[1,2- α ]quinoline in colon cancer cells depending on their p53 status. Molecular Cancer Therapeutics. 7(7). 2121–2132. 39 indexed citations

17.

Rehrauer, Hubert, Susan Schönmann, Leo Eberl, & Ralph Schlapbach. (2008). PhyloDetect: a likelihood-based strategy for detecting microorganisms with diagnostic microarrays. Bioinformatics. 24(16). i83–i89. 11 indexed citations

18.

Spanaus, Katharina, Florian Kronenberg, Eberhard Ritz, et al.. (2007). B-Type Natriuretic Peptide Concentrations Predict the Progression of Nondiabetic Chronic Kidney Disease: The Mild-to-Moderate Kidney Disease Study. Clinical Chemistry. 53(7). 1264–1272. 106 indexed citations

19.

Tweedie‐Cullen, Ry Y., Magdalena Livingstone-Zatchej, Peter Gehrig, et al.. (2006). Qualitative and Quantitative Analyses of Protein Phosphorylation in Naive and Stimulated Mouse Synaptosomal Preparations. Molecular & Cellular Proteomics. 6(2). 283–293. 187 indexed citations

20.

Spanaus, Katharina, Ralph Schlapbach, & A. Fontana. (1998). TNF-α and IFN-γ render microglia sensitive to Fas ligand-induced apoptosis by induction of Fas expression and down-regulation of Bcl-2 and Bcl-xL. European Journal of Immunology. 28(12). 4398–4408. 135 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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