Polina Mamoshina

5.0k total citations · 2 hit papers
24 papers, 2.9k citations indexed

About

Polina Mamoshina is a scholar working on Molecular Biology, Computational Theory and Mathematics and Aging. According to data from OpenAlex, Polina Mamoshina has authored 24 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 6 papers in Aging. Recurrent topics in Polina Mamoshina's work include Computational Drug Discovery Methods (6 papers), Genetics, Aging, and Longevity in Model Organisms (6 papers) and Nutritional Studies and Diet (4 papers). Polina Mamoshina is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Genetics, Aging, and Longevity in Model Organisms (6 papers) and Nutritional Studies and Diet (4 papers). Polina Mamoshina collaborates with scholars based in United States, United Kingdom and Russia. Polina Mamoshina's co-authors include Alex Zhavoronkov, Evgeny Putin, Alexander Aliper, Armando Vieira, Artem V. Artemov, Quentin Vanhaelen, Alex Aliper, Alvaro Ulloa, Sergey Plis and Fedor Galkin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Trends in Pharmacological Sciences.

In The Last Decade

Polina Mamoshina

24 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Applications of Deep Learning in Biomedicine

2016 464 citations Polina Mamoshina, Armando Vieira et al. Molecular Pharmaceutics profile →
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

2016 363 citations Alexander Aliper, Artem V. Artemov et al. Molecular Pharmaceutics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Polina Mamoshina United States	19	1.2k	817	381	328	299	24	2.9k
Alexander Aliper Russia	29	1.4k 1.2×	790 1.0×	314 0.8×	206 0.6×	294 1.0×	58	3.1k
Evgeny Putin Russia	11	719 0.6×	500 0.6×	343 0.9×	186 0.6×	173 0.6×	13	1.7k
Alex Zhavoronkov United States	49	4.0k 3.3×	2.1k 2.5×	1.1k 2.9×	613 1.9×	1.0k 3.4×	232	8.9k
Chris T. Evelo Netherlands	40	4.0k 3.3×	722 0.9×	199 0.5×	194 0.6×	42 0.1×	175	6.9k
Honghuang Lin United States	35	2.0k 1.6×	400 0.5×	100 0.3×	130 0.4×	35 0.1×	195	4.1k
Anil Wipat United Kingdom	31	2.7k 2.2×	182 0.2×	105 0.3×	239 0.7×	166 0.6×	158	4.9k
Yasushi Okuno Japan	43	3.3k 2.7×	1.2k 1.5×	428 1.1×	272 0.8×	37 0.1×	218	6.7k
Sean D. Mooney United States	39	3.7k 3.0×	154 0.2×	124 0.3×	270 0.8×	286 1.0×	140	6.0k
Kenneth N. Ross United States	33	7.2k 6.0×	1.4k 1.7×	119 0.3×	741 2.3×	59 0.2×	60	10.7k
Bairong Shen China	40	3.7k 3.0×	346 0.4×	225 0.6×	310 0.9×	23 0.1×	316	6.1k

Countries citing papers authored by Polina Mamoshina

Since Specialization

Citations

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

Fields of papers citing papers by Polina Mamoshina

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Polina Mamoshina

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Galkin, Fedor, Kirill Kochetov, Polina Mamoshina, & Alex Zhavoronkov. (2021). Adapting Blood DNA Methylation Aging Clocks for Use in Saliva Samples With Cell-type Deconvolution. SHILAP Revista de lepidopterología. 2. 697254–697254. 4 indexed citations

Mamoshina, Polina, Blanca Rodríguez, & Alfonso Bueno‐Orovio. (2021). Toward a broader view of mechanisms of drug cardiotoxicity. Cell Reports Medicine. 2(3). 100216–100216. 85 indexed citations

Galkin, Fedor, et al.. (2021). DeepMAge: A Methylation Aging Clock Developed with Deep Learning. Aging and Disease. 12(5). 1252–1252. 84 indexed citations

Galkin, Fedor, et al.. (2021). Increased Pace of Aging in COVID-Related Mortality. Life. 11(8). 730–730. 8 indexed citations

Galkin, Fedor, Polina Mamoshina, Alex Aliper, et al.. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience. 23(6). 101199–101199. 135 indexed citations

Mamoshina, Polina, Alfonso Bueno‐Orovio, & Blanca Rodríguez. (2020). Dual Transcriptomic and Molecular Machine Learning Predicts all Major Clinical Forms of Drug Cardiotoxicity. Frontiers in Pharmacology. 11. 639–639. 19 indexed citations

Galkin, Fedor, Polina Mamoshina, Alex Aliper, et al.. (2020). Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities. Ageing Research Reviews. 60. 101050–101050. 122 indexed citations

Mamoshina, Polina, Kirill Kochetov, Franco Cortese, et al.. (2019). Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Scientific Reports. 9(1). 142–142. 66 indexed citations

Zhavoronkov, Alex & Polina Mamoshina. (2019). Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity. Trends in Pharmacological Sciences. 40(8). 546–549. 44 indexed citations

10.

Mamoshina, Polina, et al.. (2018). Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. Frontiers in Genetics. 9. 242–242. 134 indexed citations

11.

Zhavoronkov, Alex, Polina Mamoshina, Quentin Vanhaelen, et al.. (2018). Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Research Reviews. 49. 49–66. 137 indexed citations

12.

Skjodt, Neil M., Polina Mamoshina, Franco Cortese, et al.. (2018). Smoking causes early biological aging: a deep neural network analysis of common blood test results. OA3809–OA3809. 1 indexed citations

13.

Mamoshina, Polina, et al.. (2017). ArabidopsisDNA topoisomerase i alpha is required for adaptive response to light and flower development. Biology Open. 6(6). 832–843. 1 indexed citations

14.

Mamoshina, Polina, Lucy O. Ojomoko, Yury Yanovich, et al.. (2017). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget. 9(5). 5665–5690. 270 indexed citations

15.

Putin, Evgeny, Polina Mamoshina, Alexander Aliper, et al.. (2016). Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging. 8(5). 1021–1033. 231 indexed citations

16.

Vanhaelen, Quentin, Polina Mamoshina, Alexander Aliper, et al.. (2016). Design of efficient computational workflows for in silico drug repurposing. Drug Discovery Today. 22(2). 210–222. 97 indexed citations

17.

Aliper, Alexander, Aleksey V. Belikov, Andrew Garazha, et al.. (2016). In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state. Aging. 8(9). 2127–2152. 45 indexed citations

18.

Mamoshina, Polina, Armando Vieira, Evgeny Putin, & Alex Zhavoronkov. (2016). Applications of Deep Learning in Biomedicine. Molecular Pharmaceutics. 13(5). 1445–1454. 464 indexed citations breakdown →

19.

Kadurin, Artur, Alexander Aliper, Andrey Kazennov, et al.. (2016). The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 8(7). 10883–10890. 213 indexed citations

20.

Mamoshina, Polina, et al.. (2015). Evolutionary divergence of Arabidopsis thaliana classical peroxidases. Biochemistry (Moscow). 80(10). 1362–1372. 4 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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