Daniel E. Webster

1.7k total citations
8 papers, 757 citations indexed

About

Daniel E. Webster is a scholar working on Molecular Biology, Oncology and Pathology and Forensic Medicine. According to data from OpenAlex, Daniel E. Webster has authored 8 papers receiving a total of 757 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Oncology and 2 papers in Pathology and Forensic Medicine. Recurrent topics in Daniel E. Webster's work include CAR-T cell therapy research (3 papers), Epigenetics and DNA Methylation (3 papers) and Cancer-related gene regulation (3 papers). Daniel E. Webster is often cited by papers focused on CAR-T cell therapy research (3 papers), Epigenetics and DNA Methylation (3 papers) and Cancer-related gene regulation (3 papers). Daniel E. Webster collaborates with scholars based in United States, United Kingdom and China. Daniel E. Webster's co-authors include Paul A. Khavari, George L. Sen, Jason Reuter, Deborah I. Barragan, Howard Y. Chang, Wenming Xiao, Masao Nakagawa, Louis M. Staudt, James D. Phelan and Roland Schmitz and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Genes & Development.

In The Last Decade

Daniel E. Webster

8 papers receiving 754 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel E. Webster United States	7	584	122	121	77	73	8	757
Tongyu Cao United States	12	435 0.7×	102 0.8×	62 0.5×	127 1.6×	35 0.5×	20	622
Albane A. Bizet Canada	6	279 0.5×	55 0.5×	114 0.9×	56 0.7×	55 0.8×	8	427
Vikram Devgan United States	8	657 1.1×	70 0.6×	306 2.5×	97 1.3×	123 1.7×	10	888
Smitha R. Georgy Australia	13	372 0.6×	40 0.3×	116 1.0×	83 1.1×	150 2.1×	21	566
Dawn M. Bryce Canada	11	601 1.0×	83 0.7×	119 1.0×	75 1.0×	101 1.4×	15	792
David Tara United States	12	400 0.7×	269 2.2×	123 1.0×	95 1.2×	70 1.0×	12	814
Tatiana M. Grzeszkiewicz United States	7	600 1.0×	112 0.9×	36 0.3×	34 0.4×	38 0.5×	8	704
Cristian Papazoglu United States	6	572 1.0×	39 0.3×	345 2.9×	81 1.1×	153 2.1×	8	801
Thomas Trenkle Germany	15	278 0.5×	126 1.0×	188 1.6×	44 0.6×	61 0.8×	27	662
Ruifeng Yang China	12	399 0.7×	48 0.4×	91 0.8×	72 0.9×	167 2.3×	32	541

Countries citing papers authored by Daniel E. Webster

Since Specialization

Citations

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

Fields of papers citing papers by Daniel E. Webster

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Webster. 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 Daniel E. Webster. The network helps show where Daniel E. Webster may publish in the future.

Co-authorship network of co-authors of Daniel E. Webster

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

All Works

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8 of 8 papers shown

1.

Webster, Daniel E., Sandrine Roulland, & James D. Phelan. (2019). Protocols for CRISPR-Cas9 Screening in Lymphoma Cell Lines. Methods in molecular biology. 1956. 337–350. 10 indexed citations

2.

Zhang, Jing‐Ping, Zhihui Song, Hongbo Wang, et al.. (2019). A novel model of controlling PD-L1 expression in ALK+ anaplastic large cell lymphoma revealed by CRISPR screening. Blood. 134(2). 171–185. 56 indexed citations

3.

Bao, Xiaomin, Zurab Siprashvili, Brian Zarnegar, et al.. (2017). CSNK1a1 Regulates PRMT1 to Maintain the Progenitor State in Self-Renewing Somatic Tissue. Developmental Cell. 43(2). 227–239.e5. 50 indexed citations

4.

Young, Ryan M., James D. Phelan, Daniel E. Webster, et al.. (2017). CRISPR‐CAS9 GENETIC SCREENS UNCOVER A B CELL RECEPTOR‐MYD88 SUPERPATHWAY IN DIFFUSE LARGE B CELL LYMPHOMA. Hematological Oncology. 35(S2). 25–25. 2 indexed citations

5.

Hodson, Daniel J., Arthur L. Shaffer, Wenming Xiao, et al.. (2016). Regulation of normal B-cell differentiation and malignant B-cell survival by OCT2. Proceedings of the National Academy of Sciences. 113(14). E2039–46. 63 indexed citations

6.

Sen, George L., et al.. (2010). DNMT1 maintains progenitor function in self-renewing somatic tissue. Nature. 463(7280). 563–567. 342 indexed citations

7.

Delgado, David, Daniel E. Webster, Kenneth B. DeSantes, Emily T. Durkin, & Aimen F. Shaaban. (2010). KIR receptor‐ligand incompatibility predicts killing of osteosarcoma cell lines by allogeneic NK cells. Pediatric Blood & Cancer. 55(7). 1300–1305. 22 indexed citations

8.

Sen, George L., Daniel E. Webster, Deborah I. Barragan, Howard Y. Chang, & Paul A. Khavari. (2008). Control of differentiation in a self-renewing mammalian tissue by the histone demethylase JMJD3. Genes & Development. 22(14). 1865–1870. 212 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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