Ashwin Narayanan
Impact in
-
- Glioma Diagnosis and Treatment
-
- Parkinson's Disease Mechanisms and Treatments
- Neurological disorders and treatments
- Neuroinflammation and Neurodegeneration Mechanisms
Papers in
- Genetics 5
- Glioma Diagnosis and Treatment 5
-
- Single-cell and spatial transcriptomics 2
- Glycosylation and Glycoproteins Research 1
- Co-authors
- Filippo Gagliardi (4 shared papers)Pietro Mortini (4 shared papers)Nicola Boari (2 shared papers)Lindsay E. Stoyka (2 shared papers)Laura A. Volpicelli‐Daley (2 shared papers)David G. Standaert (2 shared papers)David Corne (1 shared paper)Alberto Franzin (1 shared paper)
- Journals
- Journal of Visualized Experiments (2 papers)Neurobiology of Disease (2 papers)Neuro-Oncology (2 papers)Critical Reviews in Oncology/Hematology (1 paper)Cell Reports Medicine (1 paper)
- Partner nations
- United StatesGermanyItaly
In The Last Decade
Ashwin Narayanan
18 papers receiving 369 citations
Peers
Comparison fields: 5 of 74
- Genetics 46
- Neurology 63
- Neurology 30
- Cancer Research 45
- Oncology 56
Countries citing papers authored by Ashwin Narayanan
This map shows the geographic impact of Ashwin Narayanan'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 Ashwin Narayanan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashwin Narayanan more than expected).
Fields of papers citing papers by Ashwin Narayanan
This network shows the impact of papers produced by Ashwin Narayanan. 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 Ashwin Narayanan. The network helps show where Ashwin Narayanan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ashwin Narayanan, 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 | 2014 | 56 | |
| 2 | 2019 | 53 | |
| 3 | 2016 | 40 | |
| 4 | 2021 | 28 | |
| 5 | 2019 | 24 | |
| 6 | 2022 | 22 | |
| 7 | 2005 | 22 | |
| 8 | 2023 | 21 | |
| 9 | 2022 | 20 | |
| 10 | 2022 | 18 | |
| 11 | 2021 | 17 | |
| 12 | 2020 | 17 | |
| 13 | 2020 | 12 | |
| 14 | 2020 | 8 | |
| 15 | 2015 | 7 | |
| 16 | 2021 | 6 | |
| 17 | 2022 | 4 | |
| 18 | 2018 | 1 | |
| 19 | 2025 | 0 | |
| 20 | 2009 | 0 |
About Ashwin Narayanan
Ashwin Narayanan is a scholar working on Genetics, Molecular Biology, Computer Vision and Pattern Recognition, Oncology and Immunology, having authored 20 papers that have together received 376 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (5 papers), Single-cell and spatial transcriptomics (2 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers), Immune cells in cancer (2 papers), Advanced Neural Network Applications (2 papers), Parkinson's Disease Mechanisms and Treatments (2 papers), Alzheimer's disease research and treatments (1 paper) and Glycosylation and Glycoproteins Research (1 paper). The work is most often cited by research in Genetics (46 citations), Neurology (63 citations), Neurology (30 citations), Cancer Research (45 citations) and Oncology (56 citations). Ashwin Narayanan has collaborated with scholars based in United States, Germany and Italy. Frequent co-authors include Filippo Gagliardi, Pietro Mortini, Nicola Boari, Lindsay E. Stoyka, Laura A. Volpicelli‐Daley, David G. Standaert, David Corne, Alberto Franzin, Elena Mazza and Michele Reni. Their work appears in journals such as Journal of Visualized Experiments, Neurobiology of Disease, Neuro-Oncology, Critical Reviews in Oncology/Hematology and Cell Reports 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.