Alejandro Pazos

5.7k citations
180 papers · 3.7k indexed · 1 hit paper · h-index 30

Alejandro Pazos

162 papers receiving 3.6k citations

Hit Papers

A review on machine learning approaches and trends in dru...264202120262022202450100150200250

Peers

Alejandro Pazos
Comparison fields: 5 of 197
  • Signal Processing 672
  • Cognitive Neuroscience 1.1k
  • Computational Theory and Mathematics 686
  • Health Informatics 30
  • Artificial Intelligence 688
Replace Alok Sharma with:
Alok Sharma Australia
Francisco Azuaje United Kingdom
Concha Bielza Spain
Jun Wang China
Long Lu United States
Robi Polikar United States
Jiayu Zhou United States
Junshui Ma United States
Vladimir Svetnik United States
Doheon Lee South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Alejandro Pazos

Since Specialization
Citations

This map shows the geographic impact of Alejandro Pazos'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 Alejandro Pazos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alejandro Pazos more than expected).

Fields of papers citing papers by Alejandro Pazos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alejandro Pazos. 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 Alejandro Pazos. The network helps show where Alejandro Pazos may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Alejandro Pazos, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alejandro Pazos Line = papers co-authored together Alejandro Pazos links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20255
3 20248
4 20241
5 20220
6 20212
7 20218
8 202115
9 20213
10 202012
11 20201
12 20195
13 201715
14 201713
15 201664
16 20156
17 201215
18 201017
19
Metodología para la manufactura de implantes craneales a partir de imágenes DICOM y tecnologías CAD/CAM/CNC
20050
20
RECOGNITION OF HUMAN MOVEMENT PATTERNS
19961

About Alejandro Pazos

Alejandro Pazos is a scholar working on Health Informatics, Computational Theory and Mathematics, Health Information Management, Artificial Intelligence and Signal Processing, having authored 180 papers that have together received 3.7k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (27 papers), Bioinformatics and Genomic Networks (20 papers), Machine Learning in Bioinformatics (19 papers), Neural Networks and Applications (16 papers), Evolutionary Algorithms and Applications (12 papers), Neural dynamics and brain function (10 papers), Spectroscopy and Chemometric Analyses (10 papers) and EEG and Brain-Computer Interfaces (10 papers). The work is most often cited by research in Signal Processing (672 citations), Cognitive Neuroscience (1.1k citations), Computational Theory and Mathematics (686 citations), Health Informatics (30 citations) and Artificial Intelligence (688 citations). Alejandro Pazos has collaborated with scholars based in Spain, Ecuador and United States. Frequent co-authors include Daniel Rivero, Ling Guo, Julián Dorado, Cristian R. Munteanu, Enrique Fernández-Blanco, Juan R. Rabuñal, Humberto González‐Díaz, Carlos Fernández-Lozano, Alejandro Puente-Castro and José Liñares-Blanco. Their work appears in journals such as Scientific Reports, Expert Systems with Applications, Applied Sciences, Neurocomputing and International Journal of Molecular Sciences.

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.

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