Sergey Klimov

583 total citations
22 papers, 340 citations indexed

About

Sergey Klimov is a scholar working on Cancer Research, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sergey Klimov has authored 22 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cancer Research, 8 papers in Oncology and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sergey Klimov's work include Breast Cancer Treatment Studies (7 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Sergey Klimov is often cited by papers focused on Breast Cancer Treatment Studies (7 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Sergey Klimov collaborates with scholars based in United States, United Kingdom and Poland. Sergey Klimov's co-authors include Ritu Aneja, Michelle D. Reid, Padmashree C.G. Rida, Uma Krishnamurti, Xiaoxian Li, Shristi Bhattarai, Guilherme Cantuaria, Karuna Mittal, Emad A. Rakha and Ruth M. O'Regan and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and PLoS ONE.

In The Last Decade

Sergey Klimov

22 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergey Klimov United States 9 148 130 99 85 84 22 340
Thierry Pécot United States 13 131 0.9× 100 0.8× 262 2.6× 63 0.7× 45 0.5× 34 502
Ioannis Roxanis United Kingdom 12 86 0.6× 75 0.6× 142 1.4× 48 0.6× 42 0.5× 17 443
Yingjian He China 12 97 0.7× 181 1.4× 106 1.1× 77 0.9× 44 0.5× 45 382
Mervi Jumppanen Finland 4 215 1.5× 180 1.4× 211 2.1× 87 1.0× 30 0.4× 5 555
Hyun Min Koh South Korea 12 97 0.7× 89 0.7× 200 2.0× 51 0.6× 41 0.5× 41 427
Íris Sawazaki‐Calone Brazil 12 159 1.1× 99 0.8× 133 1.3× 115 1.4× 44 0.5× 14 509
Rohan P. Joshi United States 10 166 1.1× 56 0.4× 151 1.5× 69 0.8× 37 0.4× 27 469
Laura M Drogowski United States 8 165 1.1× 94 0.7× 135 1.4× 30 0.4× 31 0.4× 9 361
Yushi Ogawa Japan 12 240 1.6× 106 0.8× 177 1.8× 64 0.8× 22 0.3× 32 467
Gillian O’Hurley Ireland 10 68 0.5× 67 0.5× 198 2.0× 53 0.6× 25 0.3× 15 369

Countries citing papers authored by Sergey Klimov

Since Specialization
Citations

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

Fields of papers citing papers by Sergey Klimov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergey Klimov

This figure shows the co-authorship network connecting the top 25 collaborators of Sergey Klimov. A scholar is included among the top collaborators of Sergey Klimov 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 Sergey Klimov. Sergey Klimov 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
1.
Sledge, George W., Takayuki Yoshino, Jennifer R. Ribeiro, et al.. (2025). Real-world evidence provides clinical insights into tissue-agnostic therapeutic approvals. Nature Communications. 16(1). 2646–2646. 4 indexed citations
2.
Klimov, Sergey, Arkadiusz Gertych, Rondell P. Graham, et al.. (2021). Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis. Frontiers in Oncology. 10. 593211–593211. 27 indexed citations
3.
Mittal, Karuna, Jaspreet Kaur, Sergey Klimov, et al.. (2020). Hypoxia-Induced Centrosome Amplification Underlies Aggressive Disease Course in HPV-Negative Oropharyngeal Squamous Cell Carcinomas. Cancers. 12(2). 517–517. 8 indexed citations
4.
Klimov, Sergey, et al.. (2019). Procedure for Assuring the Continuity of Critical Information Infrastructure under Conditions of Information Influence. Voprosy kiberbezopasnosti. 37–48. 5 indexed citations
5.
Klimov, Sergey, Alton B. Farris, Hao Chen, Yaobing Chen, & Yiguo Jiang. (2019). THU-083-Predicting advanced liver fibrosis using deep learning based biopsy image analysis. Journal of Hepatology. 70(1). e196–e196. 1 indexed citations
6.
Klimov, Sergey, Islam M. Miligy, Arkadiusz Gertych, et al.. (2019). A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Research. 21(1). 83–83. 43 indexed citations
7.
Bhattarai, Shristi, Sergey Klimov, Mohammed A. Aleskandarany, et al.. (2019). Machine learning-based prediction of breast cancer growth rate in vivo. British Journal of Cancer. 121(6). 497–504. 12 indexed citations
8.
Klimov, Sergey, et al.. (2018). The method of rational dispatching a sequence of heterogeneous repair works. 63(4). 3 indexed citations
9.
Klimov, Sergey, et al.. (2018). Probabilistic Modeling of Robotic and Automated Systems Operating in Cosmic Space. DEStech Transactions on Computer Science and Engineering. 5 indexed citations
10.
Klimov, Sergey, Padmashree C.G. Rida, Mohammed A. Aleskandarany, et al.. (2017). Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers. British Journal of Cancer. 117(6). 826–834. 16 indexed citations
11.
Xia, Jun, Guilherme Cantuaria, Sergey Klimov, et al.. (2017). Distinctions in Breast Tumor Recurrence Patterns Post-Therapy among Racially Distinct Populations. PLoS ONE. 12(1). e0170095–e0170095. 7 indexed citations
12.
Ing, Nathan, Fangjin Huang, Andrew B. Conley, et al.. (2017). A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome. Scientific Reports. 7(1). 13190–13190. 29 indexed citations
13.
14.
Mittal, Karuna, Sergey Klimov, Shrikant Pawar, et al.. (2016). A centrosome clustering protein, KIFC1, predicts aggressive disease course in serous ovarian adenocarcinomas. Journal of Ovarian Research. 9(1). 17–17. 46 indexed citations
15.
Mittal, Karuna, Sergey Klimov, Shrikant Pawar, et al.. (2016). A centrosome clustering protein, KIFC1, predicts aggressive disease course in serous ovarian adenocarcinomas. IUScholarWorks (Indiana University). 2 indexed citations
16.
Li, Xiaoxian, Uma Krishnamurti, Shristi Bhattarai, et al.. (2016). Biomarkers Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. American Journal of Clinical Pathology. 145(6). 871–878. 71 indexed citations
17.
Bhattarai, Shristi, Jun Xia, Sergey Klimov, et al.. (2016). A multi-institutional study of racial differences in androgen receptor status among triple-negative breast cancers.. Journal of Clinical Oncology. 34(15_suppl). 1089–1089. 1 indexed citations
18.
Mittal, Karuna, Sergey Klimov, Shrikant Pawar, et al.. (2016). Evaluation of centrosome clustering protein KIFC1 as a potential prognostic biomarker in serous ovarian adenocarcinomas.. Journal of Clinical Oncology. 34(15_suppl). e17083–e17083. 2 indexed citations
19.
Aneja, Ritu, Karuna Mittal, Vaishali Pannu, et al.. (2015). A centrosome clustering protein, HSET, as a potential biomarker for ovarian adenocarcinomas.. Journal of Clinical Oncology. 33(15_suppl). e16562–e16562. 1 indexed citations
20.
Pannu, Vaishali, Karuna Mittal, Guilherme Cantuaria, et al.. (2015). Rampant centrosome amplification underlies more aggressive disease course of triple negative breast cancers. Oncotarget. 6(12). 10487–10497. 53 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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