Daniela A. Bota
Impact in
Papers in
- Genetics 95
- Glioma Diagnosis and Treatment 94
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- Ubiquitin and proteasome pathways 15
- Mitochondrial Function and Pathology 11
- Co-authors
- Kelvin J.A. Davies (7 shared papers)Kaijun Di (22 shared papers)Jenny K. Ngo (2 shared papers)Holly Van Remmen (1 shared paper)Naomi Lomeli (20 shared papers)Mark E. Linskey (8 shared papers)Daniela Alexandru (15 shared papers)Xing Gong (5 shared papers)
- Journals
- Neuro-Oncology (32 papers)Journal of Clinical Oncology (18 papers)CNS Oncology (6 papers)Journal of Neuro-Oncology (5 papers)Cancer Research (5 papers)
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
Daniela A. Bota
134 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Genetics 1.1k
- Aging 118
- Health Informatics 34
- Molecular Biology 1.5k
- Cancer Research 321
Countries citing papers authored by Daniela A. Bota
This map shows the geographic impact of Daniela A. Bota'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 Daniela A. Bota with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela A. Bota more than expected).
Fields of papers citing papers by Daniela A. Bota
This network shows the impact of papers produced by Daniela A. Bota. 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 Daniela A. Bota. The network helps show where Daniela A. Bota may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniela A. Bota, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 149 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 460 | |
| 2 | Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas Hit paper breakdown → | 2018 | 325 |
| 3 | 2002 | 195 | |
| 4 | 2004 | 170 | |
| 5 | 2016 | 126 | |
| 6 | 2014 | 119 | |
| 7 | 2016 | 117 | |
| 8 | 2015 | 107 | |
| 9 | 2014 | 86 | |
| 10 | 2001 | 84 | |
| 11 | 2007 | 81 | |
| 12 | 2011 | 80 | |
| 13 | 2018 | 69 | |
| 14 | 2017 | 68 | |
| 15 | 2019 | 67 | |
| 16 | 2015 | 59 | |
| 17 | 2012 | 58 | |
| 18 | 2013 | 56 | |
| 19 | 2018 | 55 | |
| 20 | 2017 | 52 |
About Daniela A. Bota
Daniela A. Bota is a scholar working on Genetics, Molecular Biology, Pulmonary and Respiratory Medicine, Oncology and Biotechnology, having authored 149 papers that have together received 3.5k indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (94 papers), Cancer Research and Treatments (20 papers), Brain Metastases and Treatment (20 papers), Cancer-related cognitive impairment studies (19 papers), Ubiquitin and proteasome pathways (15 papers), Immunotherapy and Immune Responses (14 papers), Mitochondrial Function and Pathology (11 papers) and Cancer Treatment and Pharmacology (10 papers). The work is most often cited by research in Genetics (1.1k citations), Aging (118 citations), Health Informatics (34 citations), Molecular Biology (1.5k citations) and Cancer Research (321 citations). Daniela A. Bota has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Kelvin J.A. Davies, Kaijun Di, Jenny K. Ngo, Holly Van Remmen, Naomi Lomeli, Mark E. Linskey, Daniela Alexandru, Xing Gong, Annick Desjardins and Daniel Chow. Their work appears in journals such as Neuro-Oncology, Journal of Clinical Oncology, CNS Oncology, Journal of Neuro-Oncology and Cancer Research.
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.