David Aparício
Impact in
- Physiology top 5%
- Adenosine and Purinergic Signaling
- Developmental Neuroscience top 10%
- Neurogenesis and neuroplasticity mechanisms
Papers in
-
- Advanced Graph Neural Networks 3
- Natural Language Processing Techniques 2
- Computational Physics and Python Applications 1
-
- Complex Network Analysis Techniques 5
- Co-authors
- Alberto Pérez-Samartı́n (1 shared paper)Elena Alberdi (1 shared paper)Olatz Pampliega (1 shared paper)Marı́a Domercq (1 shared paper)Carlos Matute (1 shared paper)Fernando Silva (7 shared papers)Pedro Ribeiro (7 shared papers)Tijana Milenković (1 shared paper)
- Journals
- Glia (1 paper)HPB (1 paper)Bioinformatics (1 paper)ACM Computing Surveys (1 paper)Applied Network Science (1 paper)
- Partner nations
- PortugalSpainUnited States
In The Last Decade
David Aparício
14 papers receiving 296 citations
Peers
Comparison fields: 5 of 72
- Physiology 70
- Developmental Neuroscience 39
- Neurology 76
- Statistical and Nonlinear Physics 56
- Endocrine and Autonomic Systems 22
Countries citing papers authored by David Aparício
This map shows the geographic impact of David Aparício'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 David Aparício with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Aparício more than expected).
Fields of papers citing papers by David Aparício
This network shows the impact of papers produced by David Aparício. 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 David Aparício. The network helps show where David Aparício may publish in the future.
Co-authors
The 12 scholars most cited alongside David Aparício, 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 | 2009 | 181 | |
| 2 | 2021 | 54 | |
| 3 | 2019 | 16 | |
| 4 | 2014 | 15 | |
| 5 | 2018 | 13 | |
| 6 | 2016 | 11 | |
| 7 | 2022 | 3 | |
| 8 | 2023 | 2 | |
| 9 | 2020 | 2 | |
| 10 | 2019 | 2 | |
| 11 | 2024 | 1 | |
| 12 | 2023 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 2023 | 1 |
About David Aparício
David Aparício is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Information Systems and Surgery, having authored 14 papers that have together received 303 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (5 papers), Advanced Graph Neural Networks (3 papers), Natural Language Processing Techniques (2 papers), Expert finding and Q&A systems (2 papers), Bioinformatics and Genomic Networks (2 papers), Computational Physics and Python Applications (1 paper), Distributed and Parallel Computing Systems (1 paper) and Thyroid and Parathyroid Surgery (1 paper). The work is most often cited by research in Physiology (70 citations), Developmental Neuroscience (39 citations), Neurology (76 citations), Statistical and Nonlinear Physics (56 citations) and Endocrine and Autonomic Systems (22 citations). David Aparício has collaborated with scholars based in Portugal, Spain and United States. Frequent co-authors include Alberto Pérez-Samartı́n, Elena Alberdi, Olatz Pampliega, Marı́a Domercq, Carlos Matute, Fernando Silva, Pedro Ribeiro, Tijana Milenković, Mariana Ramos Almeida and Pedro Bizarro. Their work appears in journals such as Glia, HPB, Bioinformatics, ACM Computing Surveys and Applied Network Science.
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.