Ivan V. Ozerov

2.9k total citations · 2 hit papers
34 papers, 1.3k citations indexed

About

Ivan V. Ozerov is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Ivan V. Ozerov has authored 34 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 10 papers in Cancer Research and 7 papers in Computational Theory and Mathematics. Recurrent topics in Ivan V. Ozerov's work include DNA Repair Mechanisms (8 papers), Carcinogens and Genotoxicity Assessment (7 papers) and Computational Drug Discovery Methods (7 papers). Ivan V. Ozerov is often cited by papers focused on DNA Repair Mechanisms (8 papers), Carcinogens and Genotoxicity Assessment (7 papers) and Computational Drug Discovery Methods (7 papers). Ivan V. Ozerov collaborates with scholars based in United States, Russia and Hong Kong. Ivan V. Ozerov's co-authors include Alex Zhavoronkov, Frank W. Pun, Evgeny Izumchenko, Artem V. Artemov, Polina Mamoshina, Alexander Aliper, David Sidransky, Andreyan N. Osipov, Ksenia Lezhnina and Franco Cortese and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Cancer Research.

In The Last Decade

Ivan V. Ozerov

34 papers receiving 1.3k 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.

AI-powered therapeutic target discovery

2023 171 citations Frank W. Pun, Ivan V. Ozerov et al. profile →
PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

2024 65 citations Petrina Kamya, Ivan V. Ozerov et al. Journal of Chemical Information and Modeling profile →

Peers

Ivan V. Ozerov

ONCOL

CTM

IMMUN

Artem V. Artemov Russia

Lydie Meheus Belgium

Yukiko Matsuoka Japan

Alex H. Wagner United States

Max Kotlyar Canada

Francesca Sacco Italy

Mirra Chung United States

S. A. Roumiantsev Russia

Roberta Bosotti Italy

Simon Cockell United Kingdom

Artem V. Artemov Russia

Ivan V. Ozerov

719 ×1.0

230 ×0.8

ONCOL

342 ×1.3

CTM

126 ×0.7

226 ×1.2

IMMUN

Citations per year, relative to Ivan V. Ozerov Ivan V. Ozerov (= 1×) peers Artem V. Artemov

Countries citing papers authored by Ivan V. Ozerov

Since Specialization

Citations

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

Fields of papers citing papers by Ivan V. Ozerov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ivan V. Ozerov

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

All Works

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20 of 20 papers shown

Mkrtchyan, Garik V., Andreyan N. Osipov, Evgeny Izumchenko, et al.. (2025). Variability in radiotherapy outcomes across cancer types: a comparative study of glioblastoma multiforme and low-grade gliomas. Aging. 17(2). 550–562. 1 indexed citations

Patel, Chirag, Adeeba Shakeel, Raghvendra Mall, et al.. (2025). Strategies for Redesigning Withdrawn Drugs to Enhance Therapeutic Efficacy and Safety: A Review. Wiley Interdisciplinary Reviews Computational Molecular Science. 15(1). 2 indexed citations

Kamya, Petrina, Ivan V. Ozerov, Frank W. Pun, et al.. (2024). PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery. Journal of Chemical Information and Modeling. 64(10). 3961–3969. 65 indexed citations breakdown →

Mkrtchyan, Garik V., Anastasia Shneyderman, Ivan V. Ozerov, et al.. (2024). Defining the progeria phenome. Aging. 16(3). 2026–2046. 3 indexed citations

Liu, Bonnie Hei Man, Vladimir Naumov, Frank W. Pun, et al.. (2023). Biomedical generative pre-trained based transformer language model for age-related disease target discovery. Aging. 15(18). 9293–9309. 15 indexed citations

Pun, Frank W., Hoi-Wing Leung, Tomas Schmauck‐Medina, et al.. (2023). A comprehensive AI‐driven analysis of large‐scale omic datasets reveals novel dual‐purpose targets for the treatment of cancer and aging. Aging Cell. 22(12). e14017–e14017. 19 indexed citations

Ren, Christopher X., Anastasia Shneyderman, Frank W. Pun, et al.. (2023). Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform. Aging. 15(8). 2863–2876. 11 indexed citations

Naumov, Vladimir, Hoi-Wing Leung, Frank W. Pun, et al.. (2023). Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery. Aging. 15(11). 4649–4666. 19 indexed citations

Pun, Frank W., Bonnie Hei Man Liu, Xi Long, et al.. (2022). Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform. Frontiers in Aging Neuroscience. 14. 59 indexed citations

10.

Mkrtchyan, Garik V., Evgeny Izumchenko, Anastasia Shneyderman, et al.. (2022). High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders. Cell Death and Disease. 13(11). 999–999. 13 indexed citations

11.

Chao, Jaime, Alex Zhavoronkov, Ivan V. Ozerov, et al.. (2021). Effector T cell responses unleashed by regulatory T cell ablation exacerbate oral squamous cell carcinoma. Cell Reports Medicine. 2(9). 100399–100399. 15 indexed citations

12.

Trujillo, Jonathan, Tejaswini Subbannayya, Nishant Agrawal, et al.. (2021). Doublecortin-Like Kinase 1 (DCLK1) Is a Novel NOTCH Pathway Signaling Regulator in Head and Neck Squamous Cell Carcinoma. Frontiers in Oncology. 11. 677051–677051. 16 indexed citations

13.

Пустовалова, Маргарита, et al.. (2019). Evaluation of the Contribution of Homologous Recombination in DNA Double-Strand Break Repair in Human Fibroblasts after Exposure to Low and Intermediate Doses of X-ray Radiation. Biology Bulletin. 46(11). 1496–1502. 7 indexed citations

14.

Пустовалова, Маргарита, А. Д. Каприн, Ivan V. Ozerov, et al.. (2019). Formation of γH2AX and pATM Foci in Human Mesenchymal Stem Cells Exposed to Low Dose-Rate Gamma-Radiation. International Journal of Molecular Sciences. 20(11). 2645–2645. 32 indexed citations

15.

Ravi, Rajani, Kimberly Noonan, Vui Pham, et al.. (2018). Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy. Nature Communications. 9(1). 741–741. 250 indexed citations

16.

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

17.

Zorin, V. L, Pavel Kopnin, Ivan V. Ozerov, et al.. (2017). Diffuse colonies of human skin fibroblasts in relation to cellular senescence and proliferation. Aging. 9(5). 1404–1413. 27 indexed citations

18.

Ozerov, Ivan V., Ksenia Lezhnina, Evgeny Izumchenko, et al.. (2016). In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nature Communications. 7(1). 13427–13427. 93 indexed citations

19.

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

20.

Пустовалова, Маргарита, Т. А. Астрелина, Ivan V. Ozerov, et al.. (2016). Accumulation of spontaneous γH2AX foci in long-term cultured mesenchymal stromal cells. Aging. 8(12). 3498–3506. 21 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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