Anna V. Mikhaylova

609 total citations
8 papers, 211 citations indexed

About

Anna V. Mikhaylova is a scholar working on Genetics, Epidemiology and Cancer Research. According to data from OpenAlex, Anna V. Mikhaylova has authored 8 papers receiving a total of 211 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Genetics, 3 papers in Epidemiology and 3 papers in Cancer Research. Recurrent topics in Anna V. Mikhaylova's work include Genetic Associations and Epidemiology (5 papers), Cancer-related molecular mechanisms research (3 papers) and Cytomegalovirus and herpesvirus research (2 papers). Anna V. Mikhaylova is often cited by papers focused on Genetic Associations and Epidemiology (5 papers), Cancer-related molecular mechanisms research (3 papers) and Cytomegalovirus and herpesvirus research (2 papers). Anna V. Mikhaylova collaborates with scholars based in United States. Anna V. Mikhaylova's co-authors include Timothy A. Thornton, Amalia Magaret, Meei‐Li Huang, Stacy Selke, Lawrence Corey, Christine Johnston, Marlene Kong, Anna Wald, Meena S. Ramchandani and John A. Hansen and has published in prestigious journals such as Nature Communications, Blood and The American Journal of Human Genetics.

In The Last Decade

Anna V. Mikhaylova

8 papers receiving 206 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anna V. Mikhaylova United States 6 115 65 46 41 31 8 211
Ben Margetts United Kingdom 5 78 0.7× 32 0.5× 20 0.4× 30 0.7× 47 1.5× 11 166
Amir L. Butt United States 6 115 1.0× 59 0.9× 37 0.8× 37 0.9× 12 0.4× 25 321
Shufang Meng China 9 52 0.5× 45 0.7× 48 1.0× 23 0.6× 43 1.4× 17 167
Maria Rosaria Zampino Italy 12 107 0.9× 25 0.4× 22 0.5× 81 2.0× 43 1.4× 16 260
Justin Brown United States 6 68 0.6× 22 0.3× 19 0.4× 43 1.0× 89 2.9× 9 249
DeGaulle I. Chigbu United States 9 107 0.9× 32 0.5× 33 0.7× 12 0.3× 29 0.9× 17 322
Juliana Silva United Kingdom 6 103 0.9× 107 1.6× 23 0.5× 33 0.8× 47 1.5× 10 222
Hans-Helmut Niller Germany 7 60 0.5× 26 0.4× 28 0.6× 35 0.9× 132 4.3× 10 205
Samhita Rao United States 4 103 0.9× 47 0.7× 23 0.5× 33 0.8× 6 0.2× 5 158
Virginie Adam France 8 60 0.5× 133 2.0× 89 1.9× 79 1.9× 62 2.0× 24 264

Countries citing papers authored by Anna V. Mikhaylova

Since Specialization
Citations

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

Fields of papers citing papers by Anna V. Mikhaylova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna V. Mikhaylova. 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 Anna V. Mikhaylova. The network helps show where Anna V. Mikhaylova may publish in the future.

Co-authorship network of co-authors of Anna V. Mikhaylova

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

All Works

8 of 8 papers shown
1.
Sun, Quan, Jiawen Chen, Anna V. Mikhaylova, et al.. (2024). Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI. Nature Communications. 15(1). 1016–1016. 17 indexed citations
2.
Ardlie, Kristin, Kent D. Taylor, Peter Durda, et al.. (2024). Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits. The American Journal of Human Genetics. 111(3). 445–455. 2 indexed citations
3.
Mikhaylova, Anna V., Chris Gignoux, Kristin Ardlie, et al.. (2023). Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. Human Genetics and Genomics Advances. 4(4). 100216–100216. 5 indexed citations
4.
Keys, Kevin L., Angel C. Y. Mak, Marquitta J. White, et al.. (2020). On the cross-population generalizability of gene expression prediction models. PLoS Genetics. 16(8). e1008927–e1008927. 31 indexed citations
5.
Mikhaylova, Anna V. & Timothy A. Thornton. (2019). Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations. Frontiers in Genetics. 10. 261–261. 27 indexed citations
6.
Hill, Joshua A., Amalia Magaret, Anna V. Mikhaylova, et al.. (2017). Outcomes of hematopoietic cell transplantation using donors or recipients with inherited chromosomally integrated HHV-6. Blood. 130(8). 1062–1069. 52 indexed citations
7.
Hill, Joshua A., Ruth Hall Sedlak, Amalia Magaret, et al.. (2017). Outcomes after Allogeneic Hematopoietic Cell Transplantation with Donors or Recipients Harboring Inherited Chromosomally Integrated HHV-6. Biology of Blood and Marrow Transplantation. 23(3). S55–S56. 1 indexed citations
8.
Ramchandani, Meena S., Marlene Kong, Stacy Selke, et al.. (2016). Herpes Simplex Virus Type 1 Shedding in Tears and Nasal and Oral Mucosa of Healthy Adults. Sexually Transmitted Diseases. 43(12). 756–760. 76 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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2026