Marisa Farber

1.9k total citations
77 papers, 1.2k citations indexed

About

Marisa Farber is a scholar working on Parasitology, Infectious Diseases and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Marisa Farber has authored 77 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Parasitology, 30 papers in Infectious Diseases and 24 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Marisa Farber's work include Vector-borne infectious diseases (44 papers), Viral Infections and Vectors (26 papers) and Vector-Borne Animal Diseases (21 papers). Marisa Farber is often cited by papers focused on Vector-borne infectious diseases (44 papers), Viral Infections and Vectors (26 papers) and Vector-Borne Animal Diseases (21 papers). Marisa Farber collaborates with scholars based in Argentina, United States and Brazil. Marisa Farber's co-authors include Leandro M. Redondo, Mariano E. Fernández-Miyakawa, Juan María Díaz Carrasco, Silvina Elizabeth Wilkowsky, Susana Torioni de Echaide, Ignacio Echaide, Paula Ruybal, Pablo Núñez Demarco, Héctor N. Torres and Laura Tomassone and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Marisa Farber

70 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marisa Farber Argentina 18 659 389 315 255 215 77 1.2k
Roberta Galuppi Italy 19 499 0.8× 421 1.1× 291 0.9× 164 0.6× 97 0.5× 102 1.2k
Ming He China 20 490 0.7× 327 0.8× 167 0.5× 287 1.1× 283 1.3× 35 1.3k
Emmanuelle Moreau France 17 736 1.1× 341 0.9× 295 0.9× 101 0.4× 81 0.4× 41 1.1k
A. G. Arijo Pakistan 11 409 0.6× 305 0.8× 254 0.8× 122 0.5× 109 0.5× 34 852
M. C. de S. Oliveira Brazil 22 716 1.1× 331 0.9× 474 1.5× 293 1.1× 140 0.7× 106 1.3k
Guangyou Yang China 21 706 1.1× 423 1.1× 181 0.6× 141 0.6× 259 1.2× 118 1.4k
Mousa Tavassoli Iran 19 600 0.9× 263 0.7× 237 0.8× 184 0.7× 95 0.4× 103 962
Xiaobin Gu China 22 779 1.2× 490 1.3× 213 0.7× 181 0.7× 308 1.4× 141 1.6k
José Reck Brazil 21 1.0k 1.5× 471 1.2× 381 1.2× 632 2.5× 161 0.7× 80 1.4k
R. G. Endris United States 26 392 0.6× 299 0.8× 285 0.9× 235 0.9× 172 0.8× 69 1.5k

Countries citing papers authored by Marisa Farber

Since Specialization
Citations

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

Fields of papers citing papers by Marisa Farber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marisa Farber

This figure shows the co-authorship network connecting the top 25 collaborators of Marisa Farber. A scholar is included among the top collaborators of Marisa Farber 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 Marisa Farber. Marisa Farber 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.
Esteves, Eliane, Marcelo B. Labruna, Petr Kopáček, et al.. (2025). Interactions between microbiota and immunity shape pathogen acquisition and fitness in Amblyomma spp. ticks. Developmental & Comparative Immunology. 172. 105493–105493.
2.
Orozco, Marcela, Karina Caimi, P.G. Blanco, et al.. (2025). Participatory surveillance reveals marsh deer mortality event during an extraordinary flood in Ibera Wetlands, Argentina. Ecosphere. 16(2).
3.
García-Lampasona, Sandra, et al.. (2023). Microbiome in soils of Mendoza: microbial resources for the development of agroecological management in viticulture. OENO One. 57(1). 191–205. 3 indexed citations
4.
5.
Puebla, Andrea, et al.. (2022). Argentine Navy Icebreaker Ship “Almirante Irizar” Sludge Microbial Composition Analysis for Biohydrogen Production. BioEnergy Research. 16(2). 1217–1228. 2 indexed citations
6.
Wilkowsky, Silvina Elizabeth, et al.. (2021). Development of highly sensitive one step-PCR tests for improved detection of B. bigemina and B. bovis. Veterinary Parasitology. 296. 109493–109493. 6 indexed citations
7.
Carrasco, Juan María Díaz, et al.. (2021). Geography as non-genetic modulation factor of chicken cecal microbiota. PLoS ONE. 16(1). e0244724–e0244724. 15 indexed citations
8.
Orozco, Marcela, et al.. (2020). A participatory surveillance of marsh deer (Blastocerus dichotomus) morbidity and mortality in Argentina: first results. BMC Veterinary Research. 16(1). 321–321. 21 indexed citations
9.
Salvador, Felipe Scassi, et al.. (2020). Use of molecular tools for the diagnosis of rangeliosis by Rangelia vitalii in Argentina: A case report. Veterinary Parasitology Regional Studies and Reports. 21. 100426–100426. 2 indexed citations
10.
11.
Bordoni, Noemí, et al.. (2019). Diagnóstico de Entamoeba polecki y su potencial impacto en las condiciones sanitarias de la producción porcina. SHILAP Revista de lepidopterología. 45(3). 373–377. 1 indexed citations
12.
Tirloni, Lucas, Antônio F. M. Pinto, Jolene K. Diedrich, et al.. (2019). Tick Gené’s organ engagement in lipid metabolism revealed by a combined transcriptomic and proteomic approach. Ticks and Tick-borne Diseases. 10(4). 787–797. 12 indexed citations
13.
Carrasco, Juan María Díaz, et al.. (2018). Tannins and Bacitracin Differentially Modulate Gut Microbiota of Broiler Chickens. BioMed Research International. 2018. 1–11. 103 indexed citations
14.
15.
Guizzo, Melina Garcia, Luís Fernando Parizi, Rodrigo Dutra Nunes, et al.. (2017). A Coxiella mutualist symbiont is essential to the development of Rhipicephalus microplus. Scientific Reports. 7(1). 17554–17554. 108 indexed citations
16.
Thompson, Carolina S., Atílio J. Mangold, Susana Torioni de Echaide, et al.. (2014). Typification of virulent and low virulence Babesia bigemina clones by 18S rRNA and rap-1c. Experimental Parasitology. 141. 98–105. 4 indexed citations
17.
Demarco, Pablo Núñez, Héctor Romero, Marisa Farber, & Eduardo P. C. Rocha. (2013). Natural Selection for Operons Depends on Genome Size. Genome Biology and Evolution. 5(11). 2242–2254. 15 indexed citations
18.
García, Julia Sabio y, Marisa Farber, Silvio Cravero, et al.. (2008). Expression of Babesia bovis rhoptry-associated protein 1 (RAP1) in Brucella abortus S19. Microbes and Infection. 10(6). 635–641. 6 indexed citations
19.
Wilkowsky, Silvina Elizabeth, Marisa Farber, Ignacio Echaide, et al.. (2008). Molecular Characterization of Babesia bovis Strains Using PCR Restriction Fragment Length Polymorphism Analysis of the msa2‐a/b Genes. Annals of the New York Academy of Sciences. 1149(1). 141–144. 8 indexed citations
20.
Dominguez, Mariana R., Osvaldo Zábal, Silvina Elizabeth Wilkowsky, et al.. (2004). Use of a Monoclonal Antibody against Babesia bovis Merozoite Surface Antigen‐2c for the Development of a Competitive ELISA Test. Annals of the New York Academy of Sciences. 1026(1). 165–170. 6 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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