Liang Schweizer

2.6k total citations
33 papers, 1.7k citations indexed

About

Liang Schweizer is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Liang Schweizer has authored 33 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 9 papers in Oncology and 5 papers in Computational Theory and Mathematics. Recurrent topics in Liang Schweizer's work include Wnt/β-catenin signaling in development and cancer (8 papers), Cancer-related gene regulation (7 papers) and Computational Drug Discovery Methods (5 papers). Liang Schweizer is often cited by papers focused on Wnt/β-catenin signaling in development and cancer (8 papers), Cancer-related gene regulation (7 papers) and Computational Drug Discovery Methods (5 papers). Liang Schweizer collaborates with scholars based in United States, Switzerland and China. Liang Schweizer's co-authors include Harold Varmus, Feng Cong, Konrad Basler, O Péter, Erich Brunner, Rebecca Lamb, Robert E. Ward, Richard G. Fehon, Mario Chamorro and R. L. Phillips and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

Liang Schweizer

29 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liang Schweizer United States 15 1.4k 254 214 159 157 33 1.7k
Daria Onichtchouk Germany 20 2.3k 1.7× 302 1.2× 388 1.8× 124 0.8× 150 1.0× 32 2.7k
Laura Buttitta United States 19 1.0k 0.7× 188 0.7× 226 1.1× 152 1.0× 170 1.1× 43 1.3k
Donald A. Bergstrom United States 18 1.6k 1.2× 251 1.0× 295 1.4× 104 0.7× 402 2.6× 44 2.2k
Christian Maercker Germany 16 755 0.6× 175 0.7× 138 0.6× 76 0.5× 174 1.1× 36 1.3k
Steven C. Pruitt United States 23 1.4k 1.0× 190 0.7× 252 1.2× 164 1.0× 275 1.8× 40 1.8k
Karel Dorey United Kingdom 16 1.1k 0.8× 239 0.9× 121 0.6× 132 0.8× 126 0.8× 20 1.7k
Mark Stapleton United States 15 1.7k 1.2× 220 0.9× 310 1.4× 217 1.4× 141 0.9× 20 2.0k
Pei Cheng China 14 1.2k 0.9× 145 0.6× 202 0.9× 63 0.4× 238 1.5× 32 1.6k
John J. Moskow United States 13 1.3k 0.9× 472 1.9× 307 1.4× 292 1.8× 130 0.8× 14 1.8k

Countries citing papers authored by Liang Schweizer

Since Specialization
Citations

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

Fields of papers citing papers by Liang Schweizer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Schweizer

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Schweizer. A scholar is included among the top collaborators of Liang Schweizer 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 Liang Schweizer. Liang Schweizer 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.
Schweizer, Liang, et al.. (2024). Abstract 6202: Integrating public single-cell transcriptomics and patient profiles to guide clinical development. Cancer Research. 84(6_Supplement). 6202–6202.
2.
3.
Qiao, Jennifer X., Mark R. Witmer, Tammy C. Wang, et al.. (2023). Exploration of macrocyclic peptide binders to the extracellular CRD domain of human receptor tyrosine kinase-like orphan receptor 1 (ROR1). Bioorganic & Medicinal Chemistry Letters. 98. 129589–129589. 3 indexed citations
4.
Stephen, Tom L., Qian Zhang, Byoung Kwon Lee, et al.. (2023). Abstract 3477: Maximizing the outcome of CD226 stimulation through targeting beyond TIGIT signaling with combination and multi-specific approaches for cancer immunotherapy. Cancer Research. 83(7_Supplement). 3477–3477. 1 indexed citations
5.
Tse, Vincent & Liang Schweizer. (2023). Global or local: The future of biotech. Drug Discovery Today. 28(5). 103528–103528. 2 indexed citations
6.
Wang, Yuan, Bingqing Shen, He Zhou, et al.. (2021). High-throughput functional screening for next-generation cancer immunotherapy using droplet-based microfluidics. Science Advances. 7(24). 86 indexed citations
7.
Wei, Shuo, Guang Yang, Juying Li, et al.. (2020). Abstract 2282: Discovery and characterization of novel TNFR2 antibodies to modulate T cell activities in immunosuppressive environment. Cancer Research. 80(16_Supplement). 2282–2282. 1 indexed citations
8.
Schweizer, Liang, et al.. (2017). Guiding principles of value creation through collaborative innovation in pharmaceutical research. Drug Discovery Today. 23(2). 213–218. 6 indexed citations
9.
Fereshteh, Mark, Xin Li, Sha Li, et al.. (2016). Development of a Human Whole Blood Screening Platform to Monitor JAK/STAT Signaling Using High-Throughput Flow Cytometry. SLAS DISCOVERY. 21(8). 866–874. 6 indexed citations
10.
Sheaffer, Amy K., Min S. Lee, Huilin Qi, et al.. (2016). A Small Molecule Inhibitor Selectively Induces Apoptosis in Cells Transformed by High Risk Human Papilloma Viruses. PLoS ONE. 11(6). e0155909–e0155909. 3 indexed citations
11.
Kirov, Stefan, Heshani Desilva, Jian Cao, et al.. (2015). Sensitivity of Small Cell Lung Cancer to BET Inhibition Is Mediated by Regulation of ASCL1 Gene Expression. Molecular Cancer Therapeutics. 14(10). 2167–2174. 72 indexed citations
12.
13.
Attar, Ricardo M., Maria Jure–Kunkel, Aaron Balog, et al.. (2009). Discovery of BMS-641988, a Novel and Potent Inhibitor of Androgen Receptor Signaling for the Treatment of Prostate Cancer. Cancer Research. 69(16). 6522–6530. 38 indexed citations
14.
Schweizer, Liang, Cheryl A. Rizzo, Thomas Spires, et al.. (2008). The androgen receptor can signal through Wnt/β-Catenin in prostate cancer cells as an adaptation mechanism to castration levels of androgens. BMC Cell Biology. 9(1). 4–4. 98 indexed citations
15.
Cong, Feng, Liang Schweizer, Mario Chamorro, & Harold Varmus. (2003). Requirement for a Nuclear Function of β-Catenin in Wnt Signaling. Molecular and Cellular Biology. 23(23). 8462–8470. 63 indexed citations
16.
Schweizer, Liang & Harold Varmus. (2003). Wnt/Wingless signaling through β-catenin requires the function of both LRP/Arrow and frizzled classes of receptors. BMC Cell Biology. 4(1). 4–4. 103 indexed citations
17.
18.
Schweizer, Liang & Konrad Basler. (1998). Drosophila ciD encodes a hybrid Pangolin/Cubitus interruptus protein that diverts the Wingless into the Hedgehog signaling pathway. Mechanisms of Development. 78(1-2). 141–151. 5 indexed citations
19.
Brunner, Erich, O Péter, Liang Schweizer, & Konrad Basler. (1997). pangolinencodes a Lef-1 homologue that acts downstream of Armadillo to transduce the Wingless signal in Drosophila. Nature. 385(6619). 829–833. 423 indexed citations
20.
Schweizer, Liang, et al.. (1995). Dynamics of maize endosperm development and DNA endoreduplication.. Proceedings of the National Academy of Sciences. 92(15). 7070–7074. 68 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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