Daniel G. Chain
Impact in
- Biological Psychiatry top 5%
- Tryptophan and brain disorders
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- Neuroscience and Neuropharmacology Research
- Neurobiology and Insect Physiology Research
Papers in
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- Ubiquitin and proteasome pathways 4
- Machine Learning in Bioinformatics 2
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- Neurobiology and Insect Physiology Research 2
- Co-authors
- Ashok N. Hegde (4 shared papers)James H. Schwartz (4 shared papers)Burkhard Pöeggeler (2 shared papers)Miguel A. Pappolla (2 shared papers)Eric R. Kandel (2 shared papers)Andrea Casadio (2 shared papers)Jorge Ghiso (1 shared paper)Blas Frangione (1 shared paper)
- Journals
- Alzheimer s & Dementia (3 papers)FEBS Letters (2 papers)Journal of Molecular Neuroscience (1 paper)Experimental Neurology (1 paper)Analytical Biochemistry (1 paper)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Daniel G. Chain
17 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 99
- Biological Psychiatry 103
- Cellular and Molecular Neuroscience 326
- Immunology and Allergy 77
- Cell Biology 195
- Molecular Biology 661
Countries citing papers authored by Daniel G. Chain
This map shows the geographic impact of Daniel G. Chain'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 G. Chain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel G. Chain more than expected).
Fields of papers citing papers by Daniel G. Chain
This network shows the impact of papers produced by Daniel G. Chain. 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 G. Chain. The network helps show where Daniel G. Chain may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel G. Chain, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1999 | 330 | |
| 2 | 1997 | 299 | |
| 3 | 1999 | 144 | |
| 4 | 2002 | 138 | |
| 5 | 1999 | 52 | |
| 6 | 1991 | 49 | |
| 7 | 1999 | 48 | |
| 8 | 1988 | 44 | |
| 9 | 1990 | 30 | |
| 10 | 1991 | 30 | |
| 11 | 1983 | 28 | |
| 12 | 1990 | 18 | |
| 13 | 2022 | 8 | |
| 14 | 1986 | 7 | |
| 15 | 2014 | 2 | |
| 16 | 2023 | 1 | |
| 17 | 2023 | 1 |
About Daniel G. Chain
Daniel G. Chain is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Pulmonary and Respiratory Medicine, Physiology and Cell Biology, having authored 17 papers that have together received 1.2k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (4 papers), Protease and Inhibitor Mechanisms (4 papers), Blood properties and coagulation (4 papers), Ubiquitin and proteasome pathways (4 papers), Machine Learning in Bioinformatics (2 papers), Biochemical effects in animals (2 papers), Neurobiology and Insect Physiology Research (2 papers) and Genetics and Neurodevelopmental Disorders (2 papers). The work is most often cited by research in Biological Psychiatry (103 citations), Cellular and Molecular Neuroscience (326 citations), Immunology and Allergy (77 citations), Cell Biology (195 citations) and Molecular Biology (661 citations). Daniel G. Chain has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Ashok N. Hegde, James H. Schwartz, Burkhard Pöeggeler, Miguel A. Pappolla, Eric R. Kandel, Andrea Casadio, Jorge Ghiso, Blas Frangione, Rawhi Omar and Shmuel Shaltiel. Their work appears in journals such as Alzheimer s & Dementia, FEBS Letters, Journal of Molecular Neuroscience, Experimental Neurology and Analytical Biochemistry.
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.