Daniel Glez‐Peña

72 papers receiving 1.1k citations

Peers

Daniel Glez‐Peña
Comparison fields: 5 of 168
  • Health Informatics 23
  • Health Information Management 32
  • Artificial Intelligence 216
  • Information Systems and Management 45
  • Oncology 157
Replace Hugo López-Fernández with:
Hugo López-Fernández Spain
Gil Alterovitz United States
Michael M. Hoffman Canada
Parantu K. Shah United States
Scott Hazelhurst South Africa
Richard Röttger Denmark
Ivan Merelli Italy
Gaurav Pandey United States
David Jackson United Kingdom
Pierre-Yves Vandenbussche Belgium
Daniel Glez‐Peña relative to Hugo López-Fernández Spain Hugo López-Fernández's profile →
Citations per field
00.5×3.2×
Hugo López-Fernández · 1×
Citations per year

Countries citing papers authored by Daniel Glez‐Peña

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Glez‐Peña

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Glez‐Peña. 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 Glez‐Peña. The network helps show where Daniel Glez‐Peña may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Glez‐Peña, 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 Daniel Glez‐Peña Line = papers co-authored together Daniel Glez‐Peña links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013107
2 201587
3 201577
4 202074
5 201861
6 202157
7 201644
8 201635
9 201733
10 201231
11 202030
12 201028
13 200926
14 200924
15 201722
16 201621
17 201321
18 201220
19 200918
20 202218

About Daniel Glez‐Peña

Daniel Glez‐Peña is a scholar working on Molecular Biology, Artificial Intelligence, Spectroscopy, Information Systems and Management and Information Systems, having authored 74 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (10 papers), Gene expression and cancer classification (10 papers), Mass Spectrometry Techniques and Applications (9 papers), Bioinformatics and Genomic Networks (9 papers), Biomedical Text Mining and Ontologies (9 papers), Scientific Computing and Data Management (8 papers), Genomics and Phylogenetic Studies (8 papers) and Metabolomics and Mass Spectrometry Studies (6 papers). The work is most often cited by research in Health Informatics (23 citations), Health Information Management (32 citations), Artificial Intelligence (216 citations), Information Systems and Management (45 citations) and Oncology (157 citations). Daniel Glez‐Peña has collaborated with scholars based in Spain, Portugal and United States. Frequent co-authors include Florentino Fdez‐Riverola, Miguel Reboiro‐Jato, Hugo López-Fernández, David G. Pisano, Gonzalo Goméz-López, Anália Lourenço, José Luís Capelo, Fernando Díaz, Hugo M. Santos and Eduardo Andrés‐León. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics, BMC Bioinformatics, Expert Systems with Applications and Nucleic Acids 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.

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