Fernando Salangsang

2.6k total citations · 2 hit papers
13 papers, 775 citations indexed

About

Fernando Salangsang is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Fernando Salangsang has authored 13 papers receiving a total of 775 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Oncology, 8 papers in Molecular Biology and 3 papers in Immunology. Recurrent topics in Fernando Salangsang's work include CAR-T cell therapy research (5 papers), Melanoma and MAPK Pathways (4 papers) and HER2/EGFR in Cancer Research (3 papers). Fernando Salangsang is often cited by papers focused on CAR-T cell therapy research (5 papers), Melanoma and MAPK Pathways (4 papers) and HER2/EGFR in Cancer Research (3 papers). Fernando Salangsang collaborates with scholars based in United States, Switzerland and United Kingdom. Fernando Salangsang's co-authors include Darrin D. Stuart, Nancy Pryer, Allison Landman, Martin McMahon, William R. Sellers, Mitchell P. Levesque, Meghna Das Thakur, Reinhard Dummer, Veronica Steri and Juan A. Cámara and has published in prestigious journals such as Nature, Blood and Nature Biotechnology.

In The Last Decade

Fernando Salangsang

12 papers receiving 766 citations

Hit Papers

Modelling vemurafenib resistance in melanoma reveals a st... 2013 2026 2017 2021 2013 2022 100 200 300 400 500
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.

  1. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance
    2013 539 citations Meghna Das Thakur, Fernando Salangsang et al. Nature profile →
  2. Modular cytokine receptor-targeting chimeras for targeted degradation of cell surface and extracellular proteins
    2022 152 citations Katarina Pance, Josef A. Gramespacher et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Salangsang United States 6 611 401 108 101 75 13 775
Andrew E. Aplin United States 15 604 1.0× 363 0.9× 64 0.6× 104 1.0× 91 1.2× 22 722
Stephen D. Eastman United States 5 506 0.8× 472 1.2× 78 0.7× 89 0.9× 103 1.4× 6 790
Matthew Ulman United States 3 441 0.7× 416 1.0× 148 1.4× 91 0.9× 86 1.1× 3 702
Madhavi Tadi United States 7 849 1.4× 429 1.1× 85 0.8× 138 1.4× 175 2.3× 7 1.0k
Allison Landman United States 9 700 1.1× 449 1.1× 193 1.8× 82 0.8× 72 1.0× 15 955
Marian M. Deuker United States 6 580 0.9× 274 0.7× 88 0.8× 117 1.2× 118 1.6× 6 742
Darin Smith United States 12 705 1.2× 316 0.8× 60 0.6× 98 1.0× 112 1.5× 13 978
Jan Martin United Kingdom 6 843 1.4× 431 1.1× 85 0.8× 146 1.4× 98 1.3× 7 1.0k
Anne Margriet Heijink Netherlands 12 896 1.5× 412 1.0× 141 1.3× 86 0.9× 32 0.4× 13 1.2k
Linda Kessler United States 14 746 1.2× 352 0.9× 75 0.7× 57 0.6× 62 0.8× 38 1.1k

Countries citing papers authored by Fernando Salangsang

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Salangsang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Salangsang

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

All Works

13 of 13 papers shown
1.
Glasgow, Jeff E., Rita P. Loudermilk, Lisa L. Kirkemo, et al.. (2024). Chemoproteomics reveals immunogenic and tumor-associated cell surface substrates of ectokinase CK2α. Cell chemical biology. 31(9). 1729–1739.e9. 2 indexed citations
2.
Patiño-Escobar, Bonell, Haley Johnson, Fernando Salangsang, et al.. (2024). Structure Guided Design of CCL27 CAR T-Cells Against CCR10 for Multiple Myeloma. Blood. 144(Supplement 1). 2036–2036. 2 indexed citations
3.
Nix, Matthew A., Huimin Geng, Rebecca C. Larson, et al.. (2023). Affinity Matured CD72 CAR-T Improves Efficacy Versus Low Antigen Density B-Cell Non-Hodgkin Lymphoma Models. Blood. 142(Supplement 1). 2068–2068. 1 indexed citations
4.
Nix, Matthew A., Benjamin J. Huang, Juan A. Cámara, et al.. (2023). Framework humanization optimizes potency of anti-CD72 nanobody CAR-T cells for B-cell malignancies. Journal for ImmunoTherapy of Cancer. 11(11). e006985–e006985. 14 indexed citations
5.
Kasap, Corynn, Bonell Patiño-Escobar, Yu-Hsiu T. Lin, et al.. (2023). Discovery and Development of CD70 As a Cellular Therapy Target in High Risk Multiple Myeloma. Blood. 142(Supplement 1). 465–465. 4 indexed citations
6.
Pance, Katarina, Josef A. Gramespacher, James R. Byrnes, et al.. (2022). Modular cytokine receptor-targeting chimeras for targeted degradation of cell surface and extracellular proteins. Nature Biotechnology. 41(2). 273–281. 152 indexed citations breakdown →
7.
8.
Majumder, Avisek, Veronica Steri, Fernando Salangsang, & Mark M. Moasser. (2020). Abstract LB-326: The role of HER3 in HER2-amplified cancers other than breast cancers. Cancer Research. 80(16_Supplement). LB–326. 4 indexed citations
9.
Thakur, Meghna Das, Fernando Salangsang, Allison Landman, et al.. (2013). Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature. 494(7436). 251–255. 539 indexed citations breakdown →
10.
Thakur, Meghna Das, Rosalie Fisher, Fernando Salangsang, et al.. (2013). Abstract LB-144: Modeling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance.. Cancer Research. 73(8_Supplement). LB–144.
11.
Stuart, Darrin D., Nanxin Li, Daniel J. Poon, et al.. (2012). Abstract 3790: Preclinical profile of LGX818: A potent and selective RAF kinase inhibitor. Cancer Research. 72(8_Supplement). 3790–3790. 41 indexed citations
12.
Venetsanakos, Eleni, Darrin D. Stuart, Nguyêñ Duy Tân, et al.. (2006). CHIR-265, a novel inhibitor that targets B-Raf and VEGFR, shows efficacy in a broad range of preclinical models. Cancer Research. 66. 1140–1140. 10 indexed citations
13.
Taverna, Pietro, Katherine G. Rendahl, Dragana Jekic-McMullen, et al.. (2006). Tezacitabine enhances the DNA-directed effects of fluoropyrimidines in human colon cancer cells and tumor xenografts. Biochemical Pharmacology. 73(1). 44–55. 5 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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