Yakov A. Tsepilov

2.0k total citations
57 papers, 758 citations indexed

About

Yakov A. Tsepilov is a scholar working on Genetics, Molecular Biology and Pharmacology. According to data from OpenAlex, Yakov A. Tsepilov has authored 57 papers receiving a total of 758 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Genetics, 15 papers in Molecular Biology and 15 papers in Pharmacology. Recurrent topics in Yakov A. Tsepilov's work include Musculoskeletal pain and rehabilitation (15 papers), Genetic Associations and Epidemiology (15 papers) and Fibromyalgia and Chronic Fatigue Syndrome Research (14 papers). Yakov A. Tsepilov is often cited by papers focused on Musculoskeletal pain and rehabilitation (15 papers), Genetic Associations and Epidemiology (15 papers) and Fibromyalgia and Chronic Fatigue Syndrome Research (14 papers). Yakov A. Tsepilov collaborates with scholars based in Russia, United Kingdom and United States. Yakov A. Tsepilov's co-authors include Yurii S. Aulchenko, Sodbo Sharapov, Maxim B. Freidin, Frances M. K. Williams, Pradeep Suri, Alexandra S. Shadrina, Lennart C. Karssen, Tatiana Shashkova, П. О. Федичев and Aleksandr Zenin and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Yakov A. Tsepilov

54 papers receiving 742 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yakov A. Tsepilov Russia 16 186 184 175 146 106 57 758
J. Carl Pallais United States 8 243 1.3× 472 2.6× 109 0.6× 54 0.4× 97 0.9× 15 1.2k
Melita Balija Croatia 16 53 0.3× 151 0.8× 46 0.3× 111 0.8× 97 0.9× 38 662
TJ Chen Taiwan 10 89 0.5× 174 0.9× 180 1.0× 157 1.1× 119 1.1× 24 967
Laura Hull United States 17 97 0.5× 381 2.1× 254 1.5× 147 1.0× 162 1.5× 21 1.5k
Bolun Cheng China 13 192 1.0× 318 1.7× 40 0.2× 105 0.7× 103 1.0× 129 837
Peter McPhedran United States 16 161 0.9× 116 0.6× 30 0.2× 108 0.7× 47 0.4× 35 816
Michael J. Diver United Kingdom 11 102 0.5× 208 1.1× 202 1.2× 57 0.4× 62 0.6× 18 1.1k
Georgios Athanasiadis Spain 15 234 1.3× 101 0.5× 22 0.1× 22 0.2× 81 0.8× 47 801
Céline Vial France 12 42 0.2× 288 1.6× 21 0.1× 108 0.7× 107 1.0× 21 988
Lam P Ly Australia 18 141 0.8× 374 2.0× 247 1.4× 90 0.6× 90 0.8× 26 1.4k

Countries citing papers authored by Yakov A. Tsepilov

Since Specialization
Citations

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

Fields of papers citing papers by Yakov A. Tsepilov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yakov A. Tsepilov. 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 Yakov A. Tsepilov. The network helps show where Yakov A. Tsepilov may publish in the future.

Co-authorship network of co-authors of Yakov A. Tsepilov

This figure shows the co-authorship network connecting the top 25 collaborators of Yakov A. Tsepilov. A scholar is included among the top collaborators of Yakov A. Tsepilov 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 Yakov A. Tsepilov. Yakov A. Tsepilov 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
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Suri, Pradeep, et al.. (2024). The association of lumbar intervertebral disc degeneration with low back pain is modified by underlying genetic propensity to pain. The Spine Journal. 25(1). 8–17. 4 indexed citations
3.
Suri, Pradeep, et al.. (2023). Repurposing Antihypertensive and Statin Medications for Spinal Pain. Spine. 48(22). 1568–1574. 1 indexed citations
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Zorkoltseva, Irina V., Nadezhda M. Belonogova, G. R. Svishcheva, et al.. (2023). Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes. Genes. 14(10). 1962–1962. 4 indexed citations
7.
Williams, Frances M. K., Olga O. Zaytseva, Maxim B. Freidin, et al.. (2023). Bidirectional Mendelian Randomization Study of Personality Traits Reveals a Positive Feedback Loop Between Neuroticism and Back Pain. Journal of Pain. 24(10). 1875–1885. 5 indexed citations
8.
Suri, Pradeep, Frances M. K. Williams, Maxim B. Freidin, et al.. (2023). Evidence of causal effects of blood pressure on back pain and back pain on type II diabetes provided by a bidirectional Mendelian randomization study. The Spine Journal. 23(8). 1161–1171. 12 indexed citations
9.
Tsepilov, Yakov A., Lennart C. Karssen, Maxim B. Freidin, et al.. (2023). Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain. Journal of Personalized Medicine. 13(6). 977–977. 3 indexed citations
10.
Shadrina, Alexandra S., Ian B. Stanaway, Gail P. Jarvik, et al.. (2022). Mendelian randomization analysis of plasma levels of CD209 and MICB proteins and the risk of varicose veins of lower extremities. PLoS ONE. 17(5). e0268725–e0268725. 2 indexed citations
11.
Svishcheva, G. R., Evgeny Tiys, Paul R. H. J. Timmers, et al.. (2022). A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits. Genes. 13(10). 1694–1694. 4 indexed citations
12.
Shashkova, Tatiana, et al.. (2022). The GWAS-MAP|ovis platform for aggregation and analysis of genome-wide association study results in sheep. Vavilov Journal of Genetics and Breeding. 26(4). 378–384. 3 indexed citations
13.
Zorkoltseva, Irina V., Alexandra S. Shadrina, Nadezhda M. Belonogova, et al.. (2021). In silico genome‐wide gene‐based association analysis reveals new genes predisposing to coronary artery disease. Clinical Genetics. 101(1). 78–86. 3 indexed citations
14.
Torgasheva, Anna A., et al.. (2021). Negative heterosis for meiotic recombination rate in spermatocytes of the domestic chicken Gallus gallus. Vavilov Journal of Genetics and Breeding. 25(6). 661–668. 2 indexed citations
15.
Волкова, Н. А., et al.. (2021). Multivariate Analysis Identifies Eight Novel Loci Associated with Meat Productivity Traits in Sheep. Genes. 12(3). 367–367. 9 indexed citations
16.
Freidin, Maxim B., Yakov A. Tsepilov, Ian B. Stanaway, et al.. (2020). Sex- and age-specific genetic analysis of chronic back pain. Pain. 162(4). 1176–1187. 24 indexed citations
17.
Tsepilov, Yakov A., Maxim B. Freidin, Alexandra S. Shadrina, et al.. (2020). Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Communications Biology. 3(1). 329–329. 39 indexed citations
18.
Lisachov, Artem, et al.. (2019). Male Meiotic Recombination in the Steppe Agama, <b><i>Trapelus sanguinolentus</i></b> (Agamidae, Iguania, Reptilia). Cytogenetic and Genome Research. 157(1-2). 107–114. 10 indexed citations
19.
Tsepilov, Yakov A., Sodbo Sharapov, Olga O. Zaytseva, et al.. (2018). A network-based conditional genetic association analysis of the human metabolome. GigaScience. 7(12). 12 indexed citations
20.
Shadrina, Alexandra S., Yakov A. Tsepilov, Е. Н. Воронина, et al.. (2018). Polymorphisms of genes involved in inflammation and blood vessel development influence the risk of varicose veins. Clinical Genetics. 94(2). 191–199. 14 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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