María Abáigar

586 total citations
16 papers, 150 citations indexed

About

María Abáigar is a scholar working on Hematology, Genetics and Molecular Biology. According to data from OpenAlex, María Abáigar has authored 16 papers receiving a total of 150 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Hematology, 7 papers in Genetics and 6 papers in Molecular Biology. Recurrent topics in María Abáigar's work include Acute Myeloid Leukemia Research (11 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (4 papers) and Hemoglobinopathies and Related Disorders (3 papers). María Abáigar is often cited by papers focused on Acute Myeloid Leukemia Research (11 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (4 papers) and Hemoglobinopathies and Related Disorders (3 papers). María Abáigar collaborates with scholars based in Spain, United Kingdom and Germany. María Abáigar's co-authors include Jesús María Hernández‐Rivas, Rocí­o Benito, María Díez‐Campelo, Fernando Ramos, M.C. del Cañizo, Francisco Campos‐Laborie, Javier De Las Rivas, Cristina Robledo, Juan Luis Garcı́a and Guillermo Sanz and has published in prestigious journals such as Blood, PLoS ONE and BMC Bioinformatics.

In The Last Decade

María Abáigar

16 papers receiving 146 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
María Abáigar Spain 7 90 82 44 25 19 16 150
Christina Ganster Germany 8 64 0.7× 59 0.7× 47 1.1× 13 0.5× 30 1.6× 17 147
Gabriela Borsaru Romania 7 111 1.2× 105 1.3× 28 0.6× 38 1.5× 15 0.8× 11 169
Corinna Albers Germany 7 89 1.0× 92 1.1× 42 1.0× 25 1.0× 8 0.4× 13 171
Laura Scifo United Kingdom 2 64 0.7× 123 1.5× 30 0.7× 24 1.0× 7 0.4× 3 152
Amritha Varshini Hanasoge Somasundara United States 5 59 0.7× 95 1.2× 50 1.1× 40 1.6× 6 0.3× 6 168
Andrew J. Menssen United States 7 172 1.9× 69 0.8× 64 1.5× 26 1.0× 18 0.9× 18 198
Moonjung Jung United States 8 55 0.6× 89 1.1× 39 0.9× 13 0.5× 20 1.1× 17 181
Nur Hezrin Shahrin Australia 5 60 0.7× 52 0.6× 39 0.9× 13 0.5× 11 0.6× 16 130
Xiaoli Mi United States 7 98 1.1× 118 1.4× 32 0.7× 22 0.9× 6 0.3× 11 187
Josephine Kahn United States 2 130 1.4× 141 1.7× 76 1.7× 28 1.1× 16 0.8× 3 236

Countries citing papers authored by María Abáigar

Since Specialization
Citations

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

Fields of papers citing papers by María Abáigar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of María Abáigar

This figure shows the co-authorship network connecting the top 25 collaborators of María Abáigar. A scholar is included among the top collaborators of María Abáigar 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 María Abáigar. María Abáigar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Dávila, Julio, Maria López‐Pavía, Esperanza Such, et al.. (2024). Outcomes and effect of somatic mutations after erythropoiesis stimulating agents in patients with lower-risk myelodysplastic syndromes. Therapeutic Advances in Hematology. 15. 1564135917–1564135917. 3 indexed citations
2.
Rodríguez‐Vicente, Ana E., María Abáigar, Rocí­o Benito, et al.. (2021). NEMHESYS—European Perspective on the Implementation of Next-generation Sequencing Into Clinical Diagnostics. HemaSphere. 5(3). e541–e541. 3 indexed citations
3.
Sandmann, Sarah, Aniek O. de Graaf, Magnus Tobiasson, et al.. (2020). Multicenter Next-Generation Sequencing Studies between Theory and Practice. Journal of Molecular Diagnostics. 23(3). 347–357. 3 indexed citations
4.
Hernández-Sánchez, Jesús María, Eva Lumbreras, María Díez‐Campelo, et al.. (2020). Genome-wide transcriptomics leads to the identification of deregulated genes after deferasirox therapy in low-risk MDS patients. The Pharmacogenomics Journal. 20(5). 664–671. 4 indexed citations
5.
Sánchez‐Barba, Mercedes, Jesús María Hernández-Sánchez, Esperanza Such, et al.. (2019). Chronic graft-versus-host disease could ameliorate the impact of adverse somatic mutations in patients with myelodysplastic syndromes and hematopoietic stem cell transplantation. Annals of Hematology. 98(9). 2151–2162. 1 indexed citations
6.
Hernández‐Sánchez, María, Jana Kotašková, Lenka Radová, et al.. (2018). CLL cells cumulate genetic aberrations prior to the first therapy even in outwardly inactive disease phase. Leukemia. 33(2). 518–558. 10 indexed citations
7.
Janusz, Kamila, Mónica Del Rey, María Abáigar, et al.. (2017). A two-step approach for sequencing spliceosome-related genes as a complementary diagnostic assay in MDS patients with ringed sideroblasts. Leukemia Research. 56. 82–87. 3 indexed citations
8.
Cedena, María‐Teresa, Inmaculada Rapado, Alejandro Santos‐Lozano, et al.. (2017). Mutations in the DNA methylation pathway and number of driver mutations predict response to azacitidine in myelodysplastic syndromes. Oncotarget. 8(63). 106948–106961. 29 indexed citations
9.
Cedena, María‐Teresa, Inmaculada Rapado, Alejandro Santos‐Lozano, et al.. (2017). Mutations in DNA Methylation Pathway and Number of Driver Mutations Predict Response to Azacitidine in Myelodysplastic Syndromes. Leukemia Research. 55. S61–S61. 3 indexed citations
10.
Aibar, Sara, et al.. (2016). Identification of expression patterns in the progression of disease stages by integration of transcriptomic data. BMC Bioinformatics. 17(S15). 432–432. 13 indexed citations
11.
Abáigar, María, Cristina Robledo, Rocí­o Benito, et al.. (2016). Chromothripsis Is a Recurrent Genomic Abnormality in High-Risk Myelodysplastic Syndromes. PLoS ONE. 11(10). e0164370–e0164370. 27 indexed citations
12.
Rey, Mónica Del, Rocí­o Benito, Celia Fontanillo, et al.. (2015). Deregulation of Genes Related to Iron and Mitochondrial Metabolism in Refractory Anemia with Ring Sideroblasts. PLoS ONE. 10(5). e0126555–e0126555. 20 indexed citations
13.
Paz, Juan F. De, et al.. (2015). aCGH-MAS: Analysis of aCGH by means of Multiagent System. BioMed Research International. 2015. 1–12. 3 indexed citations
14.
Abáigar, María, Fernando Ramos, Rocí­o Benito, et al.. (2013). Prognostic impact of the number of methylated genes in myelodysplastic syndromes and acute myeloid leukemias treated with azacytidine. Annals of Hematology. 92(11). 1543–1552. 14 indexed citations
15.
Benito, Rocí­o, Eva Lumbreras, María Abáigar, et al.. (2012). Imatinib therapy of chronic myeloid leukemia restores the expression levels of key genes for DNA damage and cell-cycle progression. Pharmacogenetics and Genomics. 22(5). 381–388. 13 indexed citations
16.
Abáigar, María, Fernando Ramos, Rocí­o Benito, et al.. (2012). Prognostic Impact of the Number of Methylated Genes in Myelodysplastic Syndromes and Acute Myeloid Leukemias Treated with 5-Azacytidine. Blood. 120(21). 1717–1717. 1 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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