Christian Webhofer

611 total citations
18 papers, 433 citations indexed

About

Christian Webhofer is a scholar working on Molecular Biology, Biological Psychiatry and Spectroscopy. According to data from OpenAlex, Christian Webhofer has authored 18 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 9 papers in Biological Psychiatry and 6 papers in Spectroscopy. Recurrent topics in Christian Webhofer's work include Metabolomics and Mass Spectrometry Studies (10 papers), Tryptophan and brain disorders (9 papers) and Advanced Proteomics Techniques and Applications (6 papers). Christian Webhofer is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (10 papers), Tryptophan and brain disorders (9 papers) and Advanced Proteomics Techniques and Applications (6 papers). Christian Webhofer collaborates with scholars based in Germany, United States and Greece. Christian Webhofer's co-authors include Christoph W. Turck, Michaela D. Filiou, Giuseppina Maccarrone, John M. Asara, Stefan Reckow, Philipp Gormanns, Inge Sillaber, Yaoyang Zhang, Marianne B. Müller and Larysa Teplytska and has published in prestigious journals such as Analytical Chemistry, Scientific Reports and Cerebral Cortex.

In The Last Decade

Christian Webhofer

18 papers receiving 431 citations

Peers

Christian Webhofer

PHYSI

SPECT

Claudia Ditzen Germany

Pandichelvam Veeraiah Netherlands

Kiran Sapkota United States

Jin‐Gang He China

Anne B. Need United States

О. К. Савушкина Russia

Shalini Dogra India

Jianing Yao United States

Václav Lisý Czechia

Claudia Ditzen Germany

Christian Webhofer

121 ×0.5

138 ×0.7

74 ×0.8

47 ×0.5

PHYSI

41 ×0.5

SPECT

Citations per year, relative to Christian Webhofer Christian Webhofer (= 1×) peers Claudia Ditzen

Countries citing papers authored by Christian Webhofer

Since Specialization

Citations

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

Fields of papers citing papers by Christian Webhofer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christian Webhofer

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

All Works

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18 of 18 papers shown

Penny, Hweixian Leong, Bin Wu, Christian Brandl, et al.. (2023). Characterization and root cause analysis of immunogenicity to pasotuxizumab (AMG 212), a prostate-specific membrane antigen-targeting bispecific T-cell engager therapy. Frontiers in Immunology. 14. 1261070–1261070. 10 indexed citations

Turck, Christoph W., Christian Webhofer, Stefan Reckow, et al.. (2022). Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry. PROTEOMICS. 22(11-12). e2100244–e2100244. 5 indexed citations

Turck, Christoph W., Christian Webhofer, Stefan Reckow, et al.. (2022). Antidepressant treatment effects on hippocampal protein turnover: molecular and spatial insights from mass spectrometry. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Park, Dong Ik, Carine Dournes, Inge Sillaber, et al.. (2017). Delineation of molecular pathway activities of the chronic antidepressant treatment response suggests important roles for glutamatergic and ubiquitin–proteasome systems. Translational Psychiatry. 7(4). e1078–e1078. 24 indexed citations

Weckmann, Katja, Michael J. Deery, Julie A. Howard, et al.. (2017). Ketamine’s antidepressant effect is mediated by energy metabolism and antioxidant defense system. Scientific Reports. 7(1). 15788–15788. 70 indexed citations

Park, Dong Ik, Carine Dournes, Inge Sillaber, et al.. (2016). Purine and pyrimidine metabolism: Convergent evidence on chronic antidepressant treatment response in mice and humans. Scientific Reports. 6(1). 35317–35317. 45 indexed citations

He, Zhisong, John M. Asara, Christian Webhofer, et al.. (2016). Fluoxetine Treatment Rescues Energy Metabolism Pathway Alterations in a Posttraumatic Stress Disorder Mouse Model. PubMed. 2(1). 46–59. 21 indexed citations

Teplytska, Larysa, João Vaz‐Silva, Chrysoula Dioli, et al.. (2016). Tau Deletion Prevents Stress-Induced Dendritic Atrophy in Prefrontal Cortex: Role of Synaptic Mitochondria. Cerebral Cortex. 27(4). bhw057–bhw057. 57 indexed citations

Turck, Christoph W., Christian Webhofer, Markus Nußbaumer, et al.. (2016). Stable isotope metabolic labeling suggests differential turnover of the DPYSL protein family. PROTEOMICS - CLINICAL APPLICATIONS. 10(12). 1269–1272. 7 indexed citations

10.

Filiou, Michaela D., et al.. (2015). Variability assessment of 15N metabolic labeling-based proteomics workflow in mouse plasma and brain. Molecular BioSystems. 11(6). 1536–1542. 3 indexed citations

11.

Reckow, Stefan & Christian Webhofer. (2014). Analysis of Individual Protein Turnover in Live Animals on a Proteome-Wide Scale. Methods in molecular biology. 1156. 147–154. 3 indexed citations

12.

Webhofer, Christian, Philipp Gormanns, Stefan Reckow, et al.. (2012). Proteomic and metabolomic profiling reveals time-dependent changes in hippocampal metabolism upon paroxetine treatment and biomarker candidates. Journal of Psychiatric Research. 47(3). 289–298. 38 indexed citations

13.

Webhofer, Christian, Yaoyang Zhang, Stefan Reckow, et al.. (2012). 15N metabolic labeling: Evidence for a stable isotope effect on plasma protein levels and peptide chromatographic retention times. Journal of Proteomics. 88. 27–33. 9 indexed citations

14.

Filiou, Michaela D., Christian Webhofer, Philipp Gormanns, et al.. (2012). The ¹⁵N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model. PROTEOMICS. 12(15-16). 2421–2427. 10 indexed citations

15.

Webhofer, Christian, Philipp Gormanns, Vladimir Tolstikov, et al.. (2011). Metabolite profiling of antidepressant drug action reveals novel drug targets beyond monoamine elevation. Translational Psychiatry. 1(12). e58–e58. 41 indexed citations

16.

Zhang, Yaoyang, Stefan Reckow, Christian Webhofer, et al.. (2011). Proteome Scale Turnover Analysis in Live Animals Using Stable Isotope Metabolic Labeling. Analytical Chemistry. 83(5). 1665–1672. 45 indexed citations

17.

Zhang, Yaoyang, Christian Webhofer, Stefan Reckow, et al.. (2009). A MS data search method for improved ¹⁵N‐labeled protein identification. PROTEOMICS. 9(17). 4265–4270. 19 indexed citations

18.

Mueller, Nikola S., Giuseppina Maccarrone, Éva Hunyadi‐Gulyás, et al.. (2008). QuantiSpec — Quantitative mass spectrometry data analysis of 15N-metabolically labeled proteins. Journal of Proteomics. 71(6). 601–608. 25 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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