Sergio Gálvez

5.5k total citations
38 papers, 737 citations indexed

About

Sergio Gálvez is a scholar working on Molecular Biology, Plant Science and Artificial Intelligence. According to data from OpenAlex, Sergio Gálvez has authored 38 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 7 papers in Plant Science and 5 papers in Artificial Intelligence. Recurrent topics in Sergio Gálvez's work include Prion Diseases and Protein Misfolding (12 papers), Genomics and Phylogenetic Studies (8 papers) and Wheat and Barley Genetics and Pathology (6 papers). Sergio Gálvez is often cited by papers focused on Prion Diseases and Protein Misfolding (12 papers), Genomics and Phylogenetic Studies (8 papers) and Wheat and Barley Genetics and Pathology (6 papers). Sergio Gálvez collaborates with scholars based in Spain, Chile and United States. Sergio Gálvez's co-authors include L Cartier, Pilar Hernández, Gabriel Dorado, D. Carleton Gajdusek, Antonio J. Martínez‐Fuentes, Colin L. Masters, Paul Brown, Mihaela Martis, Jaroslav Doležel and Miroslav Valárik and has published in prestigious journals such as Bioinformatics, PLoS ONE and Neurology.

In The Last Decade

Sergio Gálvez

33 papers receiving 711 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sergio Gálvez Spain 15 462 221 170 104 86 38 737
Yidan Liu China 15 412 0.9× 261 1.2× 93 0.5× 24 0.2× 58 0.7× 59 967
Patrick J. Gannon United States 13 327 0.7× 238 1.1× 108 0.6× 85 0.8× 18 0.2× 19 955
Li-San Wang United States 10 355 0.8× 34 0.2× 67 0.4× 72 0.7× 110 1.3× 13 536
Arne De Roeck Belgium 6 317 0.7× 43 0.2× 45 0.3× 26 0.3× 158 1.8× 8 546
Swati Mishra United States 15 453 1.0× 251 1.1× 83 0.5× 36 0.3× 52 0.6× 42 882
Shuan‐Yow Li Taiwan 18 544 1.2× 175 0.8× 94 0.6× 22 0.2× 188 2.2× 51 955
Wijit Kiatipattanasakul Japan 12 139 0.3× 74 0.3× 116 0.7× 21 0.2× 47 0.5× 17 373
Jianhua Yang China 14 546 1.2× 390 1.8× 27 0.2× 11 0.1× 38 0.4× 25 1.1k
Sungjae Kim South Korea 13 460 1.0× 69 0.3× 29 0.2× 29 0.3× 135 1.6× 24 759
Huiyan Huang United States 14 548 1.2× 110 0.5× 33 0.2× 16 0.2× 56 0.7× 27 837

Countries citing papers authored by Sergio Gálvez

Since Specialization
Citations

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

Fields of papers citing papers by Sergio Gálvez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergio Gálvez

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Gálvez. A scholar is included among the top collaborators of Sergio Gálvez based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sergio Gálvez. Sergio Gálvez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Kamberaj, Hiqmet, Sergio Gálvez, Eduard Petlenkov, et al.. (2024). Transversal skills in applied Artificial Intelligence - the case of the financial sector. e-mentor. 104(2). 82–90.
3.
Gálvez, Sergio, Ignacio Solís, Fernando Martínez–Moreno, et al.. (2024). High-throughput phenotyping using hyperspectral indicators supports the genetic dissection of yield in durum wheat grown under heat and drought stress. Frontiers in Plant Science. 15. 1470520–1470520. 3 indexed citations
4.
Gálvez, Sergio, Federico Agostini, Rubén Sancho, et al.. (2021). Comparative Genomics, Evolution, and Drought-Induced Expression of Dehydrin Genes in Model Brachypodium Grasses. Plants. 10(12). 2664–2664. 17 indexed citations
5.
Cagirici, H. Busra, Sergio Gálvez, Taner Z. Sen, & Hikmet Budak. (2021). LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants. Functional & Integrative Genomics. 21(2). 195–204. 18 indexed citations
6.
Gálvez, Sergio, et al.. (2021). BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization. Frontiers in Genetics. 12. 618659–618659. 6 indexed citations
7.
Liu, Guozheng, Sang He, V. González-Dugo, et al.. (2019). Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain. PLoS ONE. 14(2). e0211718–e0211718. 32 indexed citations
8.
Gálvez, Sergio, C. Camino, Philippa Borrill, et al.. (2018). Hotspots in the genomic architecture of field drought responses in wheat as breeding targets. Functional & Integrative Genomics. 19(2). 295–309. 34 indexed citations
9.
Díaz, David, et al.. (2017). MC64-Cluster: Many-Core CPU Cluster Architecture and Performance Analysis in B-Tree Searches. The Computer Journal. 61(6). 912–925. 2 indexed citations
10.
Díaz, David, et al.. (2014). MC64-ClustalWP2: A Highly-Parallel Hybrid Strategy to Align Multiple Sequences in Many-Core Architectures. PLoS ONE. 9(4). e94044–e94044. 9 indexed citations
11.
Díaz, David, et al.. (2012). Direct approaches to exploit many-core architecture in bioinformatics. Future Generation Computer Systems. 29(1). 15–26. 8 indexed citations
12.
Hernández, Pilar, Mihaela Martis, Gabriel Dorado, et al.. (2011). Next‐generation sequencing and syntenic integration of flow‐sorted arms of wheat chromosome 4A exposes the chromosome structure and gene content. The Plant Journal. 69(3). 377–386. 112 indexed citations
13.
Luís, José, et al.. (2000). Increasing the quality of hotel management information systems by applying workflow technology.. Information Technology & Tourism. 3(2). 87–98. 5 indexed citations
14.
Brown, Paul, Sergio Gálvez, Lev G. Goldfarb, et al.. (1992). Familial Creutzfeldt-Jakob disease in Chile is associated with the codon 200 mutation of the PRNP amyloid precursor gene on chromosome 20. Journal of the Neurological Sciences. 112(1-2). 65–67. 44 indexed citations
15.
Goldfarb, L. G., Paul Brown, Lynn R. Goldin, et al.. (1991). Creutzfeldt-Jacob disease associated with the PRNP codon 200LYS mutation: An analysis of 45 families. European Journal of Epidemiology. 7(5). 477–486. 96 indexed citations
16.
Gálvez, Sergio & Lola Cartier. (1987). [Clinical analysis of a series of 69 definitive cases of Jakob-Creutzfeldt disease occurring in Chile between 1960 and 1985].. PubMed. 115(12). 1148–54. 4 indexed citations
17.
Cartier, L, Sergio Gálvez, & D. C. Gajdusek. (1985). Familial clustering of the ataxic form of Creutzfeldt-Jakob disease with Hirano bodies.. Journal of Neurology Neurosurgery & Psychiatry. 48(3). 234–238. 23 indexed citations
18.
Gálvez, Sergio, Colin L. Masters, & D. Carleton Gajdusek. (1980). Descriptive Epidemiology of Creutzfeldt-Jakob Disease in Chile. Archives of Neurology. 37(1). 11–14. 44 indexed citations
19.
Gálvez, Sergio, et al.. (1980). [Experimental transmission of Creutzfeldt-Jakob disease to the guinea pig (author's transl)].. PubMed. 108(4). 299–303. 1 indexed citations
20.
Gálvez, Sergio, et al.. (1979). Cerebrospinal fluid and serum immunoglobulins and C3 in Creutzfeldt‐Jakob disease. Neurology. 29(12). 1610–1612. 10 indexed citations

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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