E N Geissler
- Immunology top 1%
- Molecular Biology top 5%
- Immunology and Allergy top 0.5%
- Genetics top 5%
- Hematology top 1%
- Co-authors
- David E. HousmanStephen J. GalliMindy TsaiKeith LangleyElizabeth S. RussellFrancis H. MartinKrisztina M. ZseboHarry Atkins
- Topics
- Mast cells and histamine (10 papers)Cell Adhesion Molecules Research (5 papers)Animal Genetics and Reproduction (4 papers)
- Partner nations
- United StatesItalyUnited Kingdom
In The Last Decade
E N Geissler
19 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Immunology 2.2k
- Molecular Biology 1.9k
- Immunology and Allergy 713
- Genetics 658
- Hematology 647
Countries citing papers authored by E N Geissler
This map shows the geographic impact of E N Geissler'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 E N Geissler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E N Geissler more than expected).
Fields of papers citing papers by E N Geissler
This network shows the impact of papers produced by E N Geissler. 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 E N Geissler. The network helps show where E N Geissler may publish in the future.
Co-authorship network of co-authors of E N Geissler
This figure shows the co-authorship network connecting the top 25 collaborators of E N Geissler. A scholar is included among the top collaborators of E N Geissler 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 E N Geissler. E N Geissler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 188 | |
| 2 | 66 | |
| 3 | 1 | |
| 4 | The kit Ligand, Stem Cell Factorbreakdown → | 516 |
| 5 | 46 | |
| 6 | 118 | |
| 7 | 55 | |
| 8 | 242 | |
| 9 | 358 | |
| 10 | 67 | |
| 11 | 54 | |
| 12 | Stem cell factor is encoded at the SI locus of the mouse and is the ligand for the c-kit tyrosine kinase receptorbreakdown → | 1248 |
| 13 | 6 | |
| 14 | The dominant-white spotting (W) locus of the mouse encodes the c-kit proto-oncogenebreakdown → | 1144 |
| 15 | 50 | |
| 16 | 107 | |
| 17 | Analysis of the hematopoietic effects of new dominant spotting (W) mutations of the mouse. I. Influence upon hematopoietic stem cells. | 39 |
| 18 | Analysis of the hematopoietic effects of new dominant spotting (W) mutations of the mouse. II. Effects on mast cell development. | 25 |
| 19 | 197 |
About E N Geissler
E N Geissler is a scholar working on Immunology and Allergy, Immunology and Small Animals, having authored 19 papers that have together received 4.5k indexed citations. Recurring topics across this work include Mast cells and histamine (10 papers), Cell Adhesion Molecules Research (5 papers) and Animal Genetics and Reproduction (4 papers). The work is most often cited by research in Immunology and Allergy (713 citations), Immunology (2.2k citations) and Hematology (647 citations). E N Geissler has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include David E. Housman, Stephen J. Galli, Stephen J. Galli, Mindy Tsai, Keith Langley, Elizabeth S. Russell, Francis H. Martin, Krisztina M. Zsebo, Harry Atkins and Virginia C. Broudy. Their work appears in journals such as Cell, Proceedings of the National Academy of Sciences and The Journal of Experimental Medicine.
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.